In 2015, Qliance still towered over all in the world of Direct Primary Care bragging with its claim of 20% overall cost reductions. Even that, of course, was quite a come down from the extravagant claims previously spewed under the Qliance banner; fond memories still linger of those heady days when the Heritage Foundation drooled over a non-existent British Medical Journal study alleged to have found that Qliance’s patients had 82% fewer surgeries, 66% fewer ED visits, but a mere 65% fewer specialist visits.
Yet, by the middle of 2016, Qliance was toppled; the pages of Forbes proclaimed the attainment of 38% reductions in medical costs by a Qliance rival, Paladina, at its clinic in Union County, NC. By November of 2016, even upstart Nextera Healthcare was bragging its “DigitalGlobe” study at the 25% level. The following month Nextera reached the brag-summit when Paladina’s Union County brag shrank to a still competitive 23%.
In early 2017, while DPC World eagerly awaited a counterpunch from the former leader, Qliance instead went bankrupt. But at least the torch had been passed!
For various reasons, including a bit of good luck, Paladina’s Union County clinic emerged in the three ensuing years as the principal poster child of the DPC movement.
In May of 2020, just as the Union County clinic’s iconic status reached it apogee, the game-changing Milliman study came along, . The tools of actuarial science, risk adjustment most prominently, were brought to bear in an independent study of the cost-effectiveness of a single clinic, the Union County clinic.
The Milliman study essentially kicked to the curb ALL prior DPC cost-effectiveness studies, including both the Paladina and Nextera studies, rejecting the lot for want of proper risk adjustment. In fact, the Milliman study found that a claims cost risk adjustment of 36% was required to account for the health differences between the studied Union County populations, more than enough to drown Paladina’s most current savings claim of 23%. The Milliman team made plain as day its estimate that Paladina’s Union County clinic program had not produced any significant cost savings at all.
The Milliman team also admonished that:
It is imperative to control for patient selection in DPC studies; otherwise, differences in cost due to underlying patient differences may be erroneously assigned as differences caused by DPC.
This admonition might have had a useful, if sobering, effect on direct primary care, if the DPC community were actually interested in advancing the movement based on the proficiency of DPC medical doctors rather than on the shamelessness of DPC spin doctors.
I can honestly say that the previous champions of DPC cost-effectiveness data, Nextera and Paladina, have met this challenge with more than mere lip service. Instead, they mixed fraud and incompetence with that lip service, and raced anew to the top of Mount Brag Bullshit.
A few months after the Milliman report was published, Nextera made the first move, bold one that coupled a brag at the 27% with a remarkable stunt:
“KPI Ninja** conducted risk score analysis in partnership with Johns Hopkins’ ACG® research team [.]” KPI Ninja’s Nextera study, page 7.
“KPI Ninja brought in the Johns Hopkins research team that has significant expertise in what is called population risk measurement. . . . We took that extra step and brought on the Johns Hopkins team that has this ability to run analysis. It’s in their wheelhouse and they applied that . . . [The] Johns Hopkins Research Team did the risk analysis[.]Nextera presentation at 2020 Hint DPC Summit meeting.
“We were not directly involved in this analysis.” Associate Director, HopkinsACG.org.
Not only was there no academic team involved in Nextera’s deeply flawed study, there was no risk adjustment actually performed. It was an heroic risk adjustment charade.
When Nextera bragged a 27% cost savings sporting both fake academic robes and fake risk adjustment, imagine how alarmed competitor Paladina – still reeling from Milliman’s conclusion that risk -adjustment brought the Union County savings down from 23% to 0% – would have felt, if they took Nextera at its word.
Don’t fret; knowing the truth of Milliman, Paladina understood at once that all they had to do was launch their own charade.
Paladina, on its website, moved in January of 2021. After its own lip service to risk adjustment and even lavish praise of the Milliman study, Paladina’s spin doctor went on to declare that “Paladina Health’s Union County client, the employer case study featured in the Milliman report, also prospered from adopting the direct primary care model. . . . Union County taxpayers saved $1.28 million in employee health care costs . . .. 23% . . ..” No matter that the Milliman team had actually exploded that very conclusion.
Consider, too, the sheer misrepresentational brilliance of the Paladina webpage’s careful selection of two raw data apples and two risk-adjusted data oranges drawn from four of each in Milliman’s basket.
Paladina is okay with using risk-adjusted data, but only when it cuts in Paladina’s favor.
Technical query. Given that Nextera ventriloquized Johns Hopkins and Paladina ventriloquized Milliman, is it still “Charades”’?
Instead of racing to the top of Mount Bullshit, why not stick to calm, truthful analysis that reveals direct primary care’s actual ability to reduce the costs of care?
[**] KPI Ninja is an “analyst” that has a special division dedicated to compiling brags for Nextera and other DPC companies. See here for more on KPI Ninja.
Montana’s last governor twice vetoed DPC legislation. He was not wrong.
Over the last month or so, DPC advocates from think-tanks of the right have trotted out the proposition that direct primary care could be “the key to addressing disparities in health care access in underserved areas of Montana facing severe shortages of primary care”. They are very excited that eight DPC clinics have “opened” in Montana in just a few years. Yet, when the very same advocates testified before the Montana legislation, they brought along some real MT DPC docs whose own testimony made it clear that what really happened is that eight existing clinics or practitioners in Montana decided to switch to subscription model care.
And, no doubt, each such D-PCP significantly reduced the size of their patient panel. Typical DPC clinicians brag about reducing patient panel sizes to one-third the size of those in traditional practices. Indeed, some members of the same pack of DPC advocates in the same hearing stressed the glories of tripled visit times.
But reducing patient panels sizes by two-thirds obviously aggravates the problem of primary care physicians shortages.
The most common response of the DPC community has been that DPC lowers burnout, lengthening primary care careers, presumably mitigating that aggravation – to some unknown degree and at some unknown point in the future.
I did some math.
Each PCP who chooses DPC and reduces patient panel sizes by two-thirds would need to triple the length of his remaining career to cover the gap he created by going DPC. And it would take decades to do so.
Assume an average career length of 20 years for a burning out PCP, with retirement at the age of 50. Let’s suppose that DPC makes PCP life so sweet that he works until he is 80 years old.
By the end of those 30 additional years, the equivalent of one-quarter of the patients he left behind by going DPC will still be left in the cold Montana snow.
To fully close the gap his switch to DPC created, he would have to work until he was a 90 year old PCP. The good news is that he would be very experienced; the bad news is that some 90 year-olds might struggle with “24/7 direct cellphone access to your direct primary care physician”.
To supplement the patent insufficiency of this bleak scenario, DPC advocates further argue that DPC will lead to increases in the percentage of young professionals choosing primary care practice instead of specialties. One of the think-tank “experts” from the Montana expedition has said that “we know” this to be the case, but provided no evidence other than the naked claim “we know”. Is this knowledge, or just speculation? Feel free tout a link to any significant evidence in the comments section below.
Even if there was hard evidence that DPC had shifted or might shift career choices toward primary care, it would still be wise to “be careful what you wish for”. Physician shortages in rural areas are not limited to primary care. To the contrary, there is ample evidence, such as this study from a Montana neighbor state, that rural communities face even more consequential shortages of specialists.
If a potentially gifted surgeon is willing to return to her roots in Whitefish, why turn her into a PCP?
In its recent report from KPI Ninja, Nextera Healthcare bragged unpersuasively about overall costs savings and reduced utilization of downstream care services. But they also bragged about the following utilization figures for a group of 754 members for whose primary care they were paid $580,868 in DPC subscription fees over the equivalent of a ten-month  study period:
1079 office visits
506 of which included additional in-office procedures at no extra cost
573 visits with no additional procedure
329 telephone visits
1868 text message encounters
To determine whether the amount paid represented a plausible value compared to what might have been spent for the same volume of comparable services if obtained through fee for service primary care physicians, we made assumptions that strongly favored Nextera at every point. For example, although the Kaiser Family Foundation compiled studies that showed, on average, that private pay rates for physician services were 143% of Medicare rates, we set our comparison rates at 179% of Medicare, corresponding to the highest value found in any of the studies Kaiser identified.
We applied that adjustment factor to Medicare rates for the services KPI Ninja had enumerated based on the following, extremely generous, correspondences.
we treated visits to a Nextera clinic in which no additional services were performed as equivalent to “Level 5” Medicare office visit for a group of patients, one-third of whom were new patients (coded 99205) and two-thirds of whom were established patients (coded 99215).
this level is rarely used for FFS office visits, i.e., about 5% of all visits; it assumes long probing visits, 40 minutes or more
our choice resulted in treating $264 per PCP visit as the FFS cost for a routine Nextera visit
in preparing its report based on its knowledge of payment rates used by the SVVSD, by contrast, KPI Ninja itself assigned a much lower valuation ($115) to an average PCP visit;
we treated Nextera’s average telephone call as equivalent to Medicare mid-level visits with an established patient (99213)
these are standard visit rates for problems up to moderate complexity
99213 is the code most commonly billed E & M code
we treated text encounters as the equivalent of Medicare e-visits performed asynchronously via patient portals (G2012)
these type of visits typically cover exchanges via portal for up to a week and require significant subsequent engagement in response to a patient inquiry
many of what KPI Ninja scored as “text encounters”, as actually delivered, would likely have fallen short of G2012
for example, KPI Ninja’s scoring rule would have counted three texts spread beyond 24 hours as two separate “text encounters”; with a spread of up to a week this would have been a single G2012
similarly, KPI Ninja’s scoring rule counted a simple text to, e.g., request a prescription refill request as a “text encounter”; a G2012 would not have been allowed for such a minimal activity
we treated those visits to a Nextera clinic in which additional services were performed as the equivalent of visits to an urgent care center costing an extreme $600.
In short, we bent over backwards to try to find higher cost correspondences to cast Nextera in a good light.
With these profoundly generous assumptions in Nextera’s favor, the private-pay fee-for-service world would still have delivered these or better services – the 40 minute visits, the phone calls, the asynchronous messaging, the in-office tasks like suturing and making arm casts – at less than 7% more than Nextera received. Computations here.
Face it. Nextera’s brand of primary care is no big deal, not really much above average. Nextera patients average 1.65 primary care office visits per year (versus a national average for all patients including the uninsured of 1.51; they get half a phone call every year, and they send or receive three annual text messages. Taking $839 a year for that level of service is not exactly a big deal.Claiming that this is some kind of patient access breakthrough is a new frontier in nonsense.
Nextera’s CEO is an acknowledged DPC leader and co-founder of the DPC Coalition. Nextera has 100+ physicians in eight states and a bevy of employer group contracts. The KPI Ninja study of Nextera is direct primary care, putting its best foot forward.
So, now we know where a decade of direct primary care “data” has arrived.
 Although the study period covered claims from a one-year period, KPI Ninja included a large number of part-year members in the studied cohort. The figures they presented reflect a membership that averaged only 10.1 months of membership in the study year. Per annum values, when presented in this post, have been correctly adjusted.
 This can be calculated from their claims, at page 16, regarding Nextera member savings on primary care visit coinsurance.
 Taking in an amount somewhat in excess of the average value of services delivered might be thought of as necessary to facilitate a direct primary care system that, while having only modest value for a large percentage of members in relatively good health, funds the more substantial needs of members in relatively poor health. This is a valuable type of financial service that can be supported by allowances both for added administrative expenses and for a reasonable profit.
That type of arrangements is usually called “insurance” and, in all jurisdictions similar arrangments have been made subject to the will of the people as expressed in law. Under current “insurance” law, for example, administration and profit amounts are limited to 15% or 20%. But even though direct primary care providers collect and pool monthly fees and use the use the pooled fees to fund variable service levels based on differing medical needs, DPC leaders insist that their clinics are not involved in “insurance”.
That move is calculated to permit DPC clinics to capture the profits, but avoid the regulations. Yet, without regulation, expansion of direct primary care would likely unlock a primary care microcosm of all the health economics problems addressed by contemporary regulation, particularly those relating to adverse selection and pre-existing conditions.
Also note that paying a fixed known price for a basket of direct primary care services does not provide fully meaningful transparency if the contents of the basket can vary depending on the purchaser’s changing health status. Every holiday season, my local rock and gem club offers a $2.00 mystery bag some rocks; the price is known but the bag is opaque. For a meaningfully transparent transaction you need to know both the price of the bag or basket and its exact contents.
In KPI Ninja’s “SchoolDistrictClaimsAnalysis” comparing claims costs under the Nextera plan and the competing fee for service (Choice) plan, the “Analyst” overlooked two major differences between the plans in how the “SchoolDistrict” pays “Claims“.
Nextera members pay post-deductible coinsurance at a 20% rate and the district pays an 80% share. But for the exact same claim by a member of the competing fee for service plan (Choice member), the split is 10% and 90%.
While both cohorts have the same $2,000 deductible, in theory, only the Choice plan members have access to an employer-paid $750 health reimbursement account that provides first dollar claims coverage, delaying the onset of the deductible and effectively reducing it to $1,250 dollar.
When two claims for exactly the same service rendered can draw different employer payments for Nextera members and Choice members, that difference payment has nothing whatever to do with Nextera’s effectiveness. Yet, the different effective rates at which claims are paid obviously have substantial effects on the total claims amounts for each group. Accordingly, a large part of any difference between the totals for the two groups is the result of SVVSD benefit design, not the result of anything Nextera does that reduces costs.
To accurately reflect only the savings attributable to Nextera, it is necessary to normalize the district’s average payment rate between the two populations. KPI Ninja did not see the need to do this.
Our method for doing this was to estimate and compare the actuarial values of the medical coverage in the two plans using the publicly available CMS actuarial value calculator developed for the Affordable Care Act’s individual market coverage.[**] We arrived at a downward adjustment from Choice plan total employer costs given by a factor of 0.905.
Correcting just this one oversight by KPI Ninja makes a difference of $311 in the overall savings claims, deflating Nextera’s brag by over one-third. See the computations here.
Every one of over two dozen claim cost comparisons in the KPI Ninja report needs this same adjustment (plus others discussed both in other posts at this blog and in the Nextera Manuscript that can be viewed through a menu item above).
A happy by-product of understanding how claims are divided between the district and its employees is that it essentially resolves KPI Ninja’s concerns about not having been provided employee payment data. Once we have put employer payments for the two groups on a normalized scale, the details of how the employer and employee divided the costs of particular claims among themselves is of little or no value in assessing Nextera’s aggregate contribution to overall savings. In other words employee cost-sharing issues need not impair our ability to evaluate Nextera’s performance.
[**] The paired plans have AV of 78.0% (Nextera plan) and 86.2% (Choice plan) , giving a ratio of 0.905. That ratio represents a conservative adjustment of the employer payments reported by KPI Ninja for two reasons. First, the computed AVs include both medical and pharmaceutical claims. For pharmaceutical claims, however, the cost sharing is identical, so pharmaceutical claims (about one-fifth of all claims) play no role in generating the difference. All of the 10.5% overall AV difference is generated from the difference between the paired medical claims, which must therefore be appreciably larger than 10.5%. Also, the study data included claims from two plan years, 2018 and 2019. The 80:20 coinsurance split for Nextera patients applied to 2018. For the 2019 calendar year, for Nextera patients, the district replaced coinsurance with copayments for several selected services, including e.g., $200 for advanced imaging and ED visits and $60 for specialist visits. See SVVSD 2019 benefits guide. We determined the actuarial value of both the 2018 Nextera plan and the 2019 Nextera plan. For 2019, the Nextera plan had an ever lower actuarial value, i.e, the net effect of the 2019 changes was to decrease overall employer payments for Nextera members.
Abstract:The Nextera “study” by KPI Ninja misappropriated the prestige of a Johns Hopkins research team to support its risk measurement claims; relied on an undisclosed and unvalidated methodology for obtaining population risk measurements; obtained highly dubious risk measurement results; and sharply mischaracterized the significance of those results. In the end, because applying even their own risk measurement results would have reflected negatively on Nextera, they never actually performed risk adjustment of the cost and utilization data under study.
It was a charade.
UPDATED, 12/16/2020. View between two rows of red asterisks (**********) below.
When KPI Ninja’s analysis of Nextera’s SVVSD clinic and the attendent social media blitz first hit the public, Nextera used the following language to misappropriate academic prestige to support the report’s handling of population risk measurement.
“KPI Ninja conducted risk score analysis in partnership with Johns Hopkins’ ACG® research team [.]” Before being sent down the rabbit hole, this language appeared in two prior versions of the study, dated 10.13.20 and 9.22.20 versions of the report. Similarly, a published program of a direct primary care summit meeting noted that, “The case study came together though partnership with KPI Ninja and the Johns Hopkins’ ACG® research team.” In a Youtube video, Nextera’s CEO declares: “[KPI Ninja] brought in the Johns Hopkins research that has significant expertise in what is called population risk measurement”. And on he goes, “We took that extra step and brought on the Johns Hopkins team that has this ability to run analysis. It’s in their wheel house and they applied that [.]” Specifically asked about adverse selection, he went on, “[The] Johns Hopkins Research Team did the risk analysis . . . and that allowed us to get to the $913 [in savings].”
Here’s a screenshot from that video.
And, here is reality.
“We were not directly involved in this analysis.” Associate Director, HopkinsACG.org.
In general, any direct primary care provider should earn some credit for acknowledging the relevance of population health metrics to assessments that compare results between direct primary care populations and other populations. Not here.
In this case, Nextera’s analyst KPI Ninja performatively measured population risk to answer critics, but stopped short of actually performing risk adjustment based on its own measurements, because doing so would have weakened Nextera’s bragging rights.
There is no indication that KPI Ninja actually performed a risk adjustment.
Big talk about risk measurement. No actual action.
KPI Ninja computed the risk scores for the two populations at 0.358 (Nextera) and 0.385 (non-Nextera), a difference of 7.5%. The appropriate way to present statistical risk adjustment work is to present unadjusted raw claims data, apply to the raw claims data the relevant calculated risk adjustments and, then present the adjusted claims and utilization data with a confidence interval to assist those using the data in making such judgments as they wish. As the landmark Milliman report on direct primary care for the Society of Actuaries shows, this is done even when presenting differences deemed not statistically significant.
Instead of following standard statistical practice KPI Ninja pronounced the difference “insignificant” and pronounced their own “interpretation” that the two populations were “comparable”, then excused itself from actually applying any risk adjustment to modify the raw claims or utilization data at all, as if no measurement had ever been made. In effect, they treated the data as if their risk measurement had yielded zero difference.
This is nonsense, not analysis. As an initial matter, in common statistical practice with which all analysts, data modelers, and academic researchers (even medical practitioners) should be generally familiar, there are rules for calculating and expressing the statistical significance of differences. KPI Ninja purports to have a crack team of analysts, data modelers, and academic researchers who should know how to do this. What number did they get? Did they bother to get one at all? And why do they not bother tell us?
Had KPI Ninja investigated the accuracy of the ACG® concurrent risk scoring model with which they developed the risk scores, they might have run across another Society of Actuaries report, this one entitled Accuracy of Claims-Based Risk Scoring Models; that document would have told them that the mean absolute error (MAE) for ACG® risk group predictions on groups in this size range was 7.3%.
The 7.5% difference KPI Ninja observed was outside that mean absolute error. While this is not ironclad proof the two populations differed, it is certainly substantial evidence that they do. If KPI Ninja’s risk measurements have any valid meaning, it is that 7.5% is a far more probable measure of the likely population differences than is zero.
In any event, as it is probable that these populations differ in health risk, it is deeply misleading to address health risk by declaring that “the two populations are comparable”.
And 7.5% of health care costs is far too large a share to ignore. Consider, again, the Milliman report on DPC for the Society of Actuaries. There, the expert team determined an overall relative risk score difference of 8.3%, and proceeded to apply appropriate risk adjustment to the claims and utilization data. Moreover, the Society of Actuaries study of risk adjustment determined that the risk adjustment methodology used by the Milliman team, “MARA Rx”, had a mean standard error of 8.3%. So, for the Milliman study the measured risk difference merely matched the mean standard error for the risk methodology Milliman selected; for the KPI Ninja study the measured risk difference exceeded the mean standard error for the risk methodology KPI Ninja selected. The case for applying risk adjustment to the data in the Nextera study is, therefore, even stronger than was the case for doing so in the Milliman study.
In a follow up commentary emphasizing the breakthrough importance of their study, the Milliam team wrote, “It is imperative to control for patient selection in DPC studies; otherwise, differences in cost due to underlying patient differences may be erroneously assigned as differences caused by DPC.” What the Milliman team did not say was, “Measure risk, find a case that needs risk adjustment even more than the one we studied, then omit any actual control for patient selection and deny the need for risk adjustment.”
So why KPI Ninja did substitute “zero adjustment” for the 7.5% adjustment indicated by their own risk measurement. Here’s a clue.
Nextera’s cost reduction brag is pegged at 27%; when deducted from 27%, 7.5% gives a hefty haircut, likely hundreds of dollars, to Nextera’s $913 cost reduction claim.
And being able to keep that trim to collar level would turn on the credibility of KPI Ninja’s own calculation of a 7.5% risk differential. But that effort, KPI Ninja first ever reported try at measuring risk, is not credible at all.
There are substantial reasons to believe that KPI Ninja’s diagnosis based risk measurements are skewed heavily in Nextera’s favor.
The Nextera population skews heavily toward children; this is entirely predictable, because Nextera employees pay $1600 per year less in premiums to add children than do non-Nextera employees. 24% of the Nextera cohort is less than 15 years old, compared with only 13% of the non-Nextera cohort. On other side of the spectrum, those over 65 were nearly four times as likely to reject Nextera. Upshot: the Nextera population is about 6.5 years younger on average and is less heavily female. Based on age and gender alone, per a landmark data set prepared by Dale Yamamoto for the Society of Actuaries, a risk score difference of about 17% could be expected, even in the absence of adverse selection.
But adverse selection is very much in play in the St. Vrain Valley School District. As described more fully in a separate post, the school district’s cost-sharing structure strongly steers those who anticipate moderate to heavy health care utilization into the non-Nextera cohort with cold hard cash, as much as $1787 of it for a single employee and twice that for a couple. This invites heavy adverse selection that would produce risk scores significantly exceeding those based on demographics alone. Even so, a comprehensive 2016 Society of Actuaries commissioned report on forty different risk scoring methodologies from eleven different vendors, including ACG®, explained that even the best risk adjustment models are not able to completely compensate for adverse selection.
The mere 7.5% risk difference between the cohorts that KPI Ninja ran across requires that the illness burden data for the two populations severely slash the risk gap indicated by age and gender alone. That suggests a perfect storm of the odd: a surfeit of younger, but relatively sick, Nextera members coupled to a surfeit of older, but relatively healthy, non-Nextera members — all working against a tsunami of adverse selection.
That defies belief, especially in light of widely-reported heavy selection bias at the Nextera clinics in the Longmont area. The report to which I refer was prepared and distributed by a surprising source — Nextera itself.
About two and one-half years before Nextera got its first recruits into the school district employee cohort studied here, Nextera enrolled new members from a similar employee population of an employer in virtually the same town. Nextera’s flagship clinic is near both employers, and employees of both use the same doctors at the same clinics. In its own “whitepaper”, Nextera reported that the employees of Digital Globe who declined Nextera had a prior claims history that was 43% larger than the prior claims history of those who chose the Nextera option.
(Interestingly, in the Society of Actuaries report on risk scoring methodology, a study of the effect of “highly adverse selection” was based on a test population with a claims cost history that was a mere 21% higher than the average. Does that make 43% astronomically adverse selection?)
Did Nextera go, in a mere two and one-half years, from attracting a very healthy population to attracting a still young population now weirdly sick beyond its years?
Or was Nextera simply right in their first big whitepaper, when they identified a heavy selection bias in Nextera’s favor, warranting an adjustment of — not 7.5% nor even 17% — 43%.
KPI Ninja’s assertion that the risk difference between the Nextera and non-Nextera populations addressed in the SVVSD report is a mere 7.5%, and “not significant”, is extremely doubtful. As we discuss below, something significantly above 17% is far more likely.
ACG® concurrent risk score measurements, the type attempted by KPI Ninja in this study, are vulnerable to a recognized form of bias that results from benefit design.
As mentioned above and described more fully in a separate post, the school district’s benefit structure strongly steers those who anticipate moderate to heavy health care utilization into the non-Nextera cohort with cold hard cash, as much as $1787 for a single member. Because of a $750 HRA, non-Nextera members have an effective $1250 deductible; nonmembers also pay only 10% in coinsurance after deductible, half that paid by Nextera members.
The ACG® technical manual notes that “where differences in ACG concurrent risk are present across healthcare organizations, it is almost universally attributable to differences in covered services reflected by different benefit levels and cost structures”. But, if different benefit designs can produce different ACG® concurrent risk score differences for equally risky populations, might there be occasions when different benefit designs will produce similar ACG® concurrent risk scores for populations that have different levels of underlying risk?
So it would seem. Members in a group with higher cost-sharing will under-present for care relative to a group with lower cost-sharing. If the higher cost sharing group was also the less risky group, this “benefit design artifact” would artificially shrink the “true” ACG® concurrent risk score gap.
This artifact is a corollary of induced utilization, and illustrates why the Milliman authors expressly called for studies of direct primary care to address induced utilization and why CMS’s “risk adjustment” processes incorporate both risk measurements and induced utilization factors.
One particular result of a benefit design artifact would be a discrepancy between concurrent risk measurements that incorporate clinical information and those that rely solely on demographics; specifically, a younger population with less generous benefits will have ACG® concurrent risk scores that make it look sicker than it is relative to an older population with more generous benefits.
The Nextera cohort is younger; it looks sicker than its years on ACG® concurrent risk scores; its benefit package requires significantly more cost-sharing; and cohort members presents less frequently for care. The Nextera cohort lands squarely atop a benefit design artifact.
On this basis alone, KPI Ninja’s measured risk difference will be too low, even without adverse selection into the non-Nextera cohort.
KPI Ninja’s risk measurements rest on undisclosed and unvalidated methods that were admittedly purpose-built by KPI Ninja to increase the risk scores of direct primary care populations. Anyone see a red flag?
As previously noted, KPI Ninja’s assertion that the risk difference between the cohorts is a mere 7.5%, and “not significant”, is extremely doubtful.
It literally required fabrication to get there.
ACG® risk adjustment, in the absence of pharma data, is fueled by standard diagnostic codes usually harvested from standard insurance claims data. But direct primary care physicians do not file insurance claims, and a great many of them actively resist entering the standard diagnostic codes used by ACG® into patient EHRs. Indeed, direct primary care doctors typically do not use the same EHR systems used by nearly all other primary care physicians. KPI Ninja has referred to a “data donut hole” of missing standard diagnostic codes which it sees as unfairly depriving direct primary care practitioners of the ability to defend themselves against charges of cherry-picking.
Milliman Actuaries are a world leader in health care analysis. The Society of Actuaries grant-funded a team from Milliman for a comprehensive study of direct primary care. That highly-qualified team ended up relying on risk measurements based on age, gender, and pharmaceutical usage in part because, after carefully addressing the data donut hole problem, they could find no satisfactory solution to it.
But KPI Ninja implicitly claims to have found the solution that eluded the Milliman team; they just do not care to tell us how it works. The cure apparently involves using “Nextera Zero Dollar Claims (EHR)” to supply the diagnostic data input to ACG® software. Nextera does not explain what “Nextera Zero Dollar Claims (EHR)” actually are. It might be — but there is no way to tell — that KPI Ninja’s technology scours EHR that typically lack diagnosis codes, even long after the EHR are written, to synthesize an equivalent to insurance claim diagnosis codes which can then be digested by ACG®.
Concerns about the reliability of such synthetic claims is precisely what lead the Milliman actuaries away from using a claims/diagnosis based methodology. KPI Ninja boldly goes exactly there, without telling us exactly how. Only a select few know the secret-sauce recipe that transformed direct primary care EHR records into data that is the equivalent of diagnosis code data harvested from the vastly different kind of diagnostic code records in claims from fee for service providers.
There is no evidence that KPI Ninja’s magical, mystery method for harvesting diagnosis code has been validated, or that KPI Ninja has the financial or analytical resources to perform a validation or, even, that KPI Ninja has ever employed or contracted a single certified actuary.
That KPI Ninja validate its methods would be of at least moderate importance, given KPI Ninja’s general business model of providing paid services to the direct primary community. But validation becomes of towering significance for risk-related data precisely because KPI Ninja’s methodology for risk data was developed for the clearly expressed purpose of helping direct primary care clinics address charges of cherry-picking by developing data specific to justifying increases in direct primary care member risk scores.
Validation in this context means that KPI Ninja should demonstrate that its methodologies are fair and accurate. Given KPI Ninja’s stated goal of increasing direct primary care risk scores, the most obviously pressing concern is that the method increases population risk scores only in proportion to actual risk.
For example, the ACG® technical manual itself warns about risk scores being manipulated by the deliberate upcoding patient risk. Even though sometimes detectible through audits, this has happened fairly often under CMS’s risk-adjusted capitation plans.
There is no evidence that KPI Ninja’s secreted data development process, whatever it may have been, included any protection from deliberate larding of the EHR by direct primary care providers. Then, too, if the “Nextera Zero Dollar Claims (EHR)” process is to any degree automated, a single bad setting or line of program code might bake risk measurement upcoding into the cake, even if the baker/programmer had only the best of intentions.
An outward manifestation of upcoding in a situation like Nextera’s would be a “perfect storm” as described above. In this regard, note that on page 7 of the study, KPI Ninja explains that its risk scoring was built from six categories of risk factors. The most sharply differing of the six, and the only one pointing to greater Nextera risk, was “hospital dominant morbidities”. These are the risk conditions that most reliably generate inpatient hospital admissions. KPI Ninja tells us that the Nextera population carried these conditions at a 37% greater rate than the other group.
Miraculously, despite KPI Ninja reporting this heightened inpatient hospitalization risk for the Nextera population on page 7, KPI Ninja reports on page 10 that Nextera reduced inpatient hospital admissions by 92.7%.It seems likely that something in Nextera’s secreted processing results in inclusion of an unusually large number of erroneous hospital dominant morbidities codes from Nextera’s EHR records.
* Note, too, that even if Nextera had kept the exact same complete EHR records as a standard FFS practice, complete with ICD-10 codes, the fact that such records need never be submitted for third-party audit — as they would for any other entity keeping such records for the purpose of risk measurement — would leave risk measurement subject to self-interested larding. (Favorable self-reports do not become less fraught after being laundered through expensive Johns Hopkins ACG® software.)
More importantly, on a broader level, developing and executing an EHR-to-claims code conversion process required that someone at KPI Ninja create and interpret uniform, objective, and precise standards for doing so. What were the standards? How were they created? Who applies them? What steps were taken to validate the process?
There are only two things we know for certain about the EHR-to-diagnostic claims process: first, that KPI Ninja essentially promised to deliver increased DPC cohort risk scores to Nextera; and, second, that Nextera paid KPI Ninja for its efforts.
No matter how good ACG® software may be in turning accurate diagnostic codes into accurate risk predictions, the risk measurements cranked out for Nextera patients can be no more valid than the diagnostic data generated by KPI Ninja’s secrets.
Because there is no real transparency on KPI Ninja’s part as to how it generates, from Nextera EHRs, the data needed for AGC® risk adjustment, and no evidence that such a methodology has been validated, it is impossible to confirm that KPI Ninja risk measurement of the Nextera cohort has ANY meaningful connection to reality.
Proper risk adjustment by itself would likely erase nearly all of Nextera’s $913 savings claim.
As mentioned above, looking solely at the age and gender distribution of the Nextera and non-Nextera cohorts and applying Dale Yamato’s landmark data set suggests that the costs of the non-Nextera cohort would run 17% higher than the Nextera cohort. But doing risk adjustment on that basis alone is equivalent to assuming that cohort membership is a serendipitous result. In reality, members select themselves into different cohorts based on their self-projections of needs for services.
SVVSD employees and their families did not pick plans based on age and gender. They pick the plan that will best meet their medical needs. Many of those with greater medical needs for expensive downstream care will realize that the non-Nextera plan is less generous to them and reject Nextera membership. When this adverse selection drives plan selection, an increase in, say, the average age of the cohort population is an indirect effect, a trailing indicator of the driving risk differential. Accordingly, the 17% figure derived from the Yamamoto data should be regarded as a floor for risk adjustment.
Even a risk-adjustment of 17% would lop off all over half ofNextera’s $913 savings claim. A risk adjustment of 36%, as was determined for a similar DPC practice in the Milliman report, would bring annual savings claims down to less than $25. If the true risk difference is reflected by the 43% difference between cohort claims histories reported previously in Nextera’s last published study (different employer, same clinic), Nextera may be the costliest move that school district ever made.
Even without taking population health risk into account, I show in other posts — especially here and here — that the KPI Ninja Nextera study still falls far short of demonstrating its $913 claims.
The Nextera “study” by KPI Ninja misappropriated the prestige of a Johns Hopkins research team to support its risk measurement claims; relied on an undisclosed and unvalidated methodology for obtaining population risk measurements; obtained highly dubious risk measurement results; and sharply mischaracterized the significance of those results. In the end, because applying even their own risk measurement results would have reflected negatively on Nextera, they never actually performed risk adjustment of the cost and utilization data under study.
Three major adjustments are needed, even without correcting the IP admit rate problem or arriving at a more reasonable risk adjustment.
Comparing data from Nextera patients and non-Nextera patients in the SVVSD programs requires three major adjustments which KPI Ninja never attempted. Computations here.
Because of the different benefit structures, the district’s claim costs for Nextera members reflect a large measure of savings, not due to Nextera, but due to the fact that the district pays less for the exact same claims from Nextera members than for “Choice” plan member warranting an downward adjustment of the district’s total costs for Choice members by a factor of 0.905.
The much richer overall benefit structure for non-Nextera also induces utilization, warranting a second downward adjustment of Choice total costs (by a factor of 0.950%
Applying all three adjustments reduces the claimed $913 savings to $255, bring the percentage savings down from 27% to less than 8%. Even that value assumes that the Nextera report was correct in its astonishing finding that the non-Nextera population of teachers and children had a IP admission rate of 246 per thousand.
The weight of external evidence suggests that supplying missing pharmacy data will not rescue any significant part of Nextera’s claim.
After acknowledging the complete absence of pharmacy cost data, KPI Ninja dismissed concern about the omission by repeatedly suggesting that inclusion could only showcase additional savings for Nextera members. The only support KPI Ninja offered for that suggestion was KPI Ninja’s trust-me account of its own, unidentified, and unpublished “research” performed in the course of paid service to the direct primary care industry.
The opposite conclusion — that pharmacy data might well reveal additional direct primary care costs — is supported by actual evidence. The only independent and well-controlled study of a direct primary care clinic, the landmark Milliman’s landmark study, found that after risk adjustment, the direct primary care cohort members had a slightly higher pharma usage than their counterparts. And a non-independent study that relied on only on propensity matched controls plainly suggests that one successful DPC strategy is to reduce high expense downstream costs through increasing medication compliance; the Iora clinic involved saw a 40% increase in pharmacy refills alongside significant reductions in various levels of hospital utilization.
Nextera’s claim to reduce the employer’s cost of chronic conditions suffers from some of the same problems as Nextera’s broadest claims — plus an even bigger one.
The report’s largest table, found on page 12, ostensibly shows various employer costs differences between Nextera patients and Choice patients associated to a selection of sixteen chronic conditions. For 10 of 16 Nextera claims employer cost reductions while, for the remaining six, Nextera confesses increased employer costs. Here is a
selected, condensed line from that table with two added amending lines. The first line of amendments applies the previously discussed adjustments to the employer’s cost for induced utilization (0.950). This adjustment cuts the supposed savings by $62, a mere warmup act.
 We omit cohort wide risk adjustment in this table to avoid the risk of over-correction, knowing that people on the same chronic conditions line have already been partially sorted on the basis of shared diagnostics. We omit the plan benefit adjustment so, in our second line of amendment, we can introduce the cost of primary care for the chronic conditions of Nextera members without fear of duplicating the portion of the primary care cost intrinsic to our global adjustment (0.905) for benefit package design.
The second amending line is added to remove additional skew that arises because for Choice members, employer claim costs may flow from both primary care payments and downstream care payments, while for Nextera members employer claim costs come only from downstream care.
Nextera members do receive primary care — some of the most expensive primary care in the world, in fact. Nextera’s subscription fees average $839 PMPY. Fair comparison of employer costs for chronic conditions requires an accounting of Nextera’s fees as part of the employer costs for chronic conditions. Including Nextera’s fees turns the chronic conditions table significantly against Nextera’s claims. Nextera has not demonstrated its ability to lower the costs of chronic disease.
The same issue infects the Nextera report’s computation of savings on E & M visits, on page 10.
The study omitted important information about how outlier claimants were addressed.
While KPI Ninja did address the problem of outlier claimants, it is frustrating to see about 40% of total spend hacked away from consideration without being told either the chosen threshold for claims outlier exclusion or the reasoning behind the particular choice made.
KPI Ninja makes clear that it excluded outlier claims amounts from the total spend of each cohort. But it is also salient whether members incurring high costs were excluded from the cohort in compiling population utilization rates or risk scores.
The analyst understood that a million-dollar member would heavily skew cost comparison. If, however, the same million-dollar member had daily infusion therapy, this could heavily skew KPI Ninja’s OP visit utilization comparison. And, if that same member and a few others had conditions with profoundly high risk coefficients that might have a significant effect on final risk scores.
The better procedure is to avoid all skew from outliers. The Milliman report excluded members with outlier claims from the cohort for all determinations, whether of cost, utilization or, even, risk. In their report, KPI Ninja addressed outlier members only in terms of their claims cost. There is no indication that KPI Ninja appropriately accounted for outlier patients in its determination of utilization rates or population risk.
A significant aspect of risk measurement may have been clarified by accounting properly for outlier. And a good chunk of that astonishing IP admit figure for Choice patients might have vanished had they done so.
A study design error by KPI Ninja further skews cost data in Nextera’s favor by a hard to estimate amount.
“The actuary should consider the effect enrollment practices (for example, the ability of participants to drop in and out of a health plan) have had on health care costs.”
But the actuarial wannabees at KPI Ninja did not do that. The only claims cost data marshaled for this study were claims for which the district made a payment. Necessarily, these were claims for which a deductible was met. Because KPI Ninja did not follow the guidance from the actuarial board, however, it ended up with two significantly different cohorts in terms of the cohort members’ ability to meet the district’s $2000 annual deductible and maximum out of pocket expense limit.
Specifically, the average time in cohort for a non-Nextera member was 11.1 months; for Nextera it was only 10.1. On average, Nextera members had twice as many shortened deductible years as non-Nextera members. And shortened deductible years mean more unmet deductibles and mOOPs, and fewer employer paid claims; in insurance lingo, this is considered less “exposure”. The upshot is that the reported employer paid claims for the two cohorts are biased in Nextera’s favor.
 Most of the difference is related to KPI Ninja’s choosing a school year for data collection when plan membership and the deductible clock runs on a calender year. Nextera has publicly bragged of seeing a boost in membership for its second plan year. Those new members had spent only eight months of plan membership when the study period ended.
To largely eliminate this distortion, KPI Ninja need only have restricted the study to members of either cohort who had experienced a complete deductible cycle. To estimate the amount of distortion after the fact is challenging, and the resulting adjustment may be too small to warrant the effort. What would make more sense would be for Nextera to just send the data where it belonged in the first place, to a real actuary who knows how to design an unbiased study.
A related error may have infected all of KPI Ninja’s utilization calculations, with potentially large consequences. KPI Ninja’s utilization reduction claims on page 10 appear not to have been normalized to correct for the difference in shortened years between the two cohorts. If they have indeed not been so adjusted, then all the service utilization numbers shown for Nextera members on that page currently need an upward adjustment of 10%. One devastating effect: this adjustment would completely erase Nextera’s claim of reducing utilization of emergency departments.
There is no evidence that the utilization data were normalized to correct for the one-month shortfall of Nextera members “in cohort dwell time”.
Summary of all current Nextera posts.
The two astonishing claims of Nextera’s school district advertising blitz are deeply misleading and unsupported. In no meaningful sense, does Nextera save 27% or $913 per year for every patient served by Nextera’s doctors rather than by the Saint Vrain Valley region’s traditional, fee-for-service primary care physicians. In no meaningful sense, do patients served by Nextera doctors have 92.7% fewer inpatient hospital admissions than those served by the Saint Vrain Valley region’s traditional, fee-for-service primary care physicians.
The KPI Ninja report on Nextera is at war with the best available evidence on direct primary care, that from the Milliman study. The KPI Ninja analysis head-faked risk adjustment, an essential feature of plan comparison, but actually performed none at all. The vast bulk of the reported cost savings turn on the dubious finding that a low risk population had a Medicare-like hospital admissions rate they could have reduced by choosing Nextera’s physicians.
An adequate study required not only risk adjustment, but also adjustments for induced utilization and for normalizing employer share cost based on the benefit plans. Combined all adjustments cut the purported Nextera savings down from $913 to $255, even accepting as given a freakishly high IP admission rate and a freakishly low risk adjustment of 7.5%.
Every single one of the report’s claims that Nextera lowered the cost of various downstream care services is tainted by one or more of these unaddressed factors.
Credibility requires a well-designed study and careful analysis, transparency, candor, and a firm understanding of the many effects of benefit design. The KPI Ninja report on Nextera had none of it. It is, at best, a spin doctor’s badly concocted puff piece.Promotion of KPI Ninja’s work on behalf of one hundred Nextera physicians — by video, by press release,and by distribution of the report by social media and otherwise — is false advertising that demands correction.
The KPI Ninja report on Nextera’s school district program claims big savings when employees chose Nextera’s direct primary care rather than traditional primary care. But the analysis reflects inadequacy of a high order. Here’s a starter course of cluelessness, actually one the report’s smaller problems.
The report ignored the effect of an HRA made available to non-Nextera members only. But $750 in first dollar coverage gets a cost-conscious non-Nextera employee a lot of cost-barrier-free primary care for her chronic condition. And, unlike the dollars the SVVSD spends at Nextera, every HRA dollar the district covers for a non-Nextera employee still applies to her deductible.
Is Nextera the best choice for her?
If she’s a math teacher at Longmont High, the odds are extremely high that she’ll figure this out, then reject Nextera.
No one, not even a KPI Ninja, can make sense of the SVVSD’s programs without considering the profound effect of the HRA — shifting costs, shifting utilization, and shifting member plan selection.
Fun – duh – mentals of plan comparison
You cannot accurately assess cost differences between plans without addressing significant differences in plan benefit design.
You cannot accurately assess utilization differences between plans without addressing significant differences in plan benefit design.
You cannot accurately assess selection bias between plans without addressing significant differences in plan benefit design.
A $750 HRA is a significant difference in plan benefit design, large enough to seriously affect a $913 savings claim.
The KPI Ninja report failed to address the HRA. For that reason alone, one might think it reasonable to disregard the report in its entirety.
But that might be too fair to KPI Ninja and Nextera. There’s lots more and it gets worse. The KPI Ninja/Nextera report is nonsense piled high.
The HRA issue and many others are discussed at length in these five posts:
KPI Ninja’s Nextera Risk Measurement Charade focuses on the study’s major failure on population health measurement issues. While Nextera and KPI Ninja bragged of risk adjustment performed by an academic research team, neither the team and nor the risk adjustment were real.
Nextera did not reduce inpatient hospital admissions by 92.7%focuses on a single astonishing utilization claim from the Nextera report, that might reflect a severe error in basic data collection — one that just by itself would account for every penny of the claimed savings. Or is it just cherry-picking at the Olympic level?
Nextera’s Next Era in Cherry-Picking Machine Designfocuses on the need for any report on the SVVSD plan to reflect the differences in benefit design. Although updated recently to bridge to the published report, its core content predates the published report by months, and it was shared in early summer 2020 with both KPI Ninja and Nextera.
By some reckoning, this is the 100th post on dpcreferee.com.
Abstract: KPI Ninja’s report on Nextera’s direct primary care plan for employees of a Colorado school district clinic claims profoundly good results: nearly $1000 per year in savings for every Nextera clinic member and a staggering 93.7% reduction in inpatient hospital admissions. Both claims rest on the proposition that a population of middle-aged. middle-class, white-color, healthy Colorado teachers, spouses, and children families experience an inpatient hospital admission rate of 246 per 1k, 30% greater than Colorado’s Medicare population.
In their path-breaking report on Direct Primary Care to the Society of Actuaries, the team from Milliman Actuaries described a model framework for an employer direct primary option. They concluded that DPC was a break-even monetary propositions when DPC monthly fees were set at an average of $60 PMPM, $720 PMPM. That modeling was based on data from the first, and still unique, wholly disinterested, actuarially sound analysis ever performed on a direct primary care clinic; the particular clinic had long been treated by the DPC community as a darling poster child; and Milliman Actuaries have an impeccable reputation.
Just months after the Milliman report, Nextera set out to entice potential employers and members with a brand new report from its analyst, KPI Ninja. That case study claimed that Nextera saved the Saint Vrain Valley School District $913 PMPY. But if Milliman was anywhere near correct when it set $60 PMPM as a break even, zero savings proposition, thena $913 PMPY savings for an even more pricey Nextera clinic looks too good to be true.
A bottom line so at war with the expectations of informed experts, like the world class Milliman Actuaries, is a red flag. It prompts close examination of the data and the analysis on which it rests.
And there it was: data on the non-Nextera population’s hospital utilization that is far too bad to be true.
If we take KPI Ninja’s risk measurement at face value, both the Nextera and non-Nextera populations were quite healthy, with both populations with likely to have medical costs well less than half those of a national reference data population (ACG® risk scores less than 0.400). This makes sense for a school district population with its likely surfeit of white collar, middle class workers. The district is also in Colorado, which has relatively low hospitalization rates compared to the nation at large — a recent report by the Colorado Hospital Association pegs the statewide rate at under 80 inpatient admissions per 1k. The KPI Ninja report puts Nextera’s own IP admit rate at a plausible 90 per 1k (not particularly laudable as it is double the IP admit rate of the DPC studied by Milliman).
On the other hand, the KPI Ninja report puts the non-Nextera inpatient hospitalization rate at 246 per 1k. That large (1590), relatively healthy, and teacher-heavy population of school district employees and their families, tracked for a full year, were presumably hospitalized at more than 3.2 times the rate of average Coloradans. Indeed, the 246/1k admissions rate KPI Ninja reports for the non-Nextera cohort, comprising mostly white collar adults and their children, with an average population age in the thirties, is nearly 30% higher than the admission rate for Coloradans receiving Medicare, a group more than three decades older.
Pooling all the patients studied by KPI Ninja from both cohorts yields a blended IP admit rate of 195/1k which is still higher than the Medicare IP admit rate of 190/1k. Given the age and gender mix in the two cohorts, application of national statistics (AHQR’s HCUP data) would predict IP admission rates of 88 (Nextera) and 96 (Non-Nextera).
That all those middle-aged adults and their kids have the same IP admit rate as a Medicare population does not pass the smell test.
There appears to be a massive error at work here, and there is enough of it to explain away all of Nextera’s $913 claims cost brag without breaking into a sweat.
Consider an alternative: what if Nextera cut inpatient hospital admissions by a “mere” third, starting from a presumptive non-Nextera IP admission rates of 136 per 1K. 136/1k is still an outsize IP admit rate for a commercial population. 136 per 1k would still be more than double the highest reported IP admit rate appearing in ANY prior study of direct primary care. And that highest report (58/1k) came from the study by the professional and fully independent Milliman actuaries.
Moreover, within the landmark Milliman study, the DPC was found to have only an IP admission rate reduction for DPC of 25%. The 136/1k I propose here for the non-Nextera corresponds to a Nextera rate reduction effect of a full one-third. Even with that generous upgrade for Nextera over Milliman, and assigning hospital costs per admission for non-Nextera patients calculated from the Nextera report ($8317), use of 136/1k wipes out every penny of the $913 cost reduction claim.
Of course, it did occur to me that perhaps the difference in hospital utilization might be accounted for if the non-Nextera population were significantly risker than the Nextera population, i.e., as if the Nextera population had been cherry-picked in the way the Milliman report anticipated. I had suggested as much in my June post reacting to an initial release of Nextera’s raw data.
But the CEO of Nextera has expressly told us by Youtube video that the Johns Hopkins ACG® Research Team found the risk difference between the populations to be statistically insignificant. In that statement, Dr. Clinton Flanagan was completely incorrect, but let’s indulge that falsehood for a moment, yet still try to account for the insanely high IP rate for non-Nextera patients.
The 90/1k IP admission rate for Nextera’s own members is nearly identical to the national average for a group of like age and gender (88/1k per HCUP, see above.) This suggests that Nextera-care is pretty ordinary, so we cannot attribute Nextera’s 90 to 246 “win” on IP admit rates to Nextera’s special magic.
So, how did the non-Nextera cohort come to have 246 IP admits per thousand?
Does the very act of eschewing Nextera cause bad health luck — cancers, infectious disease, car crashes, moose attacks, etc?
Here’s an idea of how much bad luck might be needed to explain that 246/1k IP admission rate. From data in the table at the bottom of page 10 in KPI Ninja’s report, assuming the data are correctly described, it can be computed that only 50 unique individuals in the 1590 member non-Nextera cohort had an IP admission. If the IP admit rate is indeed 246 per 1k, then the full cohort would have had 391 admits. That’s an average of almost eight admits per year for each of the 50 patients who had one or more admissions. That’s a heck of a lot of bad health luck.
If not bad luck, then perhaps bad doctors. Is the fee for service primary care physician community in the Saint Vrain Valley incompetent?
One thing that has always struck me is how the DPC community drifts so easily into impugning its fee-for-service competitors. Attributing a 246 per 1k hospital admit rate to the patients of the local FFS community libels those practitioners.
The Nextera report, a Nextera press release, and a YouTube video all directly claim a 92.7% reduction in IP admit rate. Warning members of the public that rejecting Nextera’s services could increase their risk of hospitalization by 1200% goes far beyond reasonable commercial “spin”. It’s misleading medical advertising that warrants investigation and sanction.
Apart from extreme cherry-picking, the most likely explanation of a seemingly insane IP admit rate is that data describing a dominating stack of school district money has been mishandled.
A reported 246 per 1k admit rate for any cohort of middle-aged, middle class, white-collar workers and their children is just too bad to be true.
The KPI Ninja report has numerous additional weaknesses, including a failure to adequately address population risk measures, benefit design, study design, and data limitations.
That red flag flies high. Nextera’s claims of $913 savings, a 92.7% reduction in inpatient hospital admissions, and both without cherry-picking are too good to be true.
Note: revised and redated for proximity to related material. Original version June 27, 2020.
In June of 2020, Nextera HealthCare had a hot new brag:
These results were not risk adjusted. But they desperately needed to be.
The St Vrain Valley School District had this health benefit structure for its employees during the period studied:
The school district’s 10% coinsurance rate for the PPO predates the arrival of the Nextera option. The school district also has a Kaiser Permanente plan that includes 10% coinsurance. The school district created the unique 20% coinsurance rate for Nextera DPC patients to help fund the added primary care investment involved. Here’s how that benefit structure impacts employees expecting various levels of usage in an coming year.
As the image above shows, Nexera reported $5,000 per year is as an average utilization level for an employee member of the district; an employee expecting $5000 in utilization can gain over $900 dollars by rejecting Nextera. Every penny of that advantage for the employee comes out of the employer’s hide — and then it shows up in Nextera’s table as a Nextera win. A employee with moderately heavy utilization – but still only about twice the average and still far short of her mOOP— might even hit the jackpot of shifting $1787 from her pocket to the employer, simply by rejecting Nextera. Heavier utilizers, those who surpass their maximum out of pocket – will all gain at least $750 by rejecting Nextera.
This benefit design pushes a large swath of risky, costly patients away from Nextera.
But that tells only part of the story. As if pushing unhealthy patients away by increasing cost-sharing does not do quite enough to steer low risk patients to Nextera, a difference between employee share of premiums specifically drives children into the Nextera cohort. A Nextera employee pays $1600 less per year to add coverage for her children than she would pay to have the same kids covered in the non-Nextera plan. About 24% Nextera population is under 15 years old, versus about 13% for the other group. On the other hand, those 65 and up are four times more likely to reject Nextera. The overall Nextera population is about 6.5 years younger on average as a result.
And notice that even after Nextera starts with a younger, healthier pool, those who elected Nextera will face vastly more cost-sharing discipline under their benefit plan than their PPO counterparts. They can be expected, in aggregate, to consume less. This is known as induced utilization. Per the Milliman team, this should be considered by those evaluating the impacts of DPC.
If the employer’s claims costs are adjusted for both (a) the youth and health risk difference between Nextera and non-Nextera populations, and (b) the confounding effect of induced utilization, Nextera’s cost savings brag will likely be shredded.
Indeed, we have good reason — from the recent Milliman study and from Nextera’s own previous study of the exact same clinic— to suspect that a population risk-adjustment of more than a third is quite likely. Adjust the Nextera brag by that third and the savings will not simply vanish, they will turn into increased costs.
In this regard, moreover, a 2016 Society of Actuaries commissioned report, explained that all the available risk adjustment models failed to completely compensate for adverse selection. Ironically, their selection of a “highly adverse” population for evaluating the performance of the major risk adjustment methodologies was one with a claims cost history that was 21% higher than the average. In Nextera’s earlier self-study of the same clinic, the prior claims cost history of the non-Nextera cohort was an astronomically adverse 43% higher than the Nextera cohort.
Update: October 22, 2020. So now Nextera has published an extended account of its SVVSD program. It’s herehere.
(It was “there” before Nextera sent down the rabbit hole it’s claim that a Johns Hopkins research team had done the cherry-picking analysis; that claim persists in this slide.)
Christian Care Ministry (“Medi-Share”), whose 400,000 members account for more than a quarter of health cost sharing members nationally, recently acted to allow some of its members to receive credit for their entire direct primary care membership fees up to $1800 per year.
That there is a certain synergy between DPC and health cost sharing plans is testified to in countless instances of mutually interested cross-promotion. But in the end, these are separate economic entities with their own bottom line financial needs.
Precisely because direct primary care entities refuse to work with actual insurers, we do not have much data from insurance companies from which we glean what their actuaries think DPC might be worth.** But a multi-billion dollar, 400k member cost-sharing entity, even if “non-insurance”, needs actuarially skilled professionals to make ends meet. So, when a major cost-sharing ministry rewards direct primary care members with a financial incentive, that may tell us what insurance companies will not.
Tell us what you really think, Medi-Share!
Only one of Christian Care Ministry’s options offers DPC benefits. That plan comes with a $12,000 annual Annual Household Portion (“AHP” is ministry-speak for “deductible”), but it allows its members to apply the full amount of their direct primary care fees toward that AHP. That could be as valuable as lowering an annual deductible from $12,000 to $10,200.
And we can easily estimate the actuarial value of that reduction. Here’s a screen shot from the Colorado ACA plan finder for 2020 for the premiums paid by a 38 year old Coloradan for two Anthem plans that differ only by $2150 in deductible. It costs $2.62 a month to reduce an annual deductible from $8150 to $6000. Necessarily, a reduction of $1800 a year cost less. As well, a reduction of any amount of deductible downward from a higher starting point will have a lesser value than that same amount from a lower starting point, i.e. , Medishare’s $1800 reduction downward from its $12,000 AHB is actuarially worth less than an $1800 reduction down from Anthem’s $8,150.
So there it is. $31 a year.
Wow. In a DPC with a $90 a month fee you’ll be spending twice as much on primary care as the average person using fee for service, but your downstream care savings are estimated by Medishare to be worth a whopping $3 a month on downstream care. It’s like getting one $1500 ED visit for free — every forty years or so.
** On the other hand, we do have the word of the former CEO of the now-defunct Qliance DPC to the effect that, for some presumably nefarious reason, insurance companies were not appropriately responsive to Qliance studies that claimed 20% overall cost savings.
HSAs are intended to encourage more cost-conscious spending by placing more of the health care financing burden on out-of-pocket spending by the users of services, as opposed to having the costs of those services incorporated in payments shared over a wider group of plan enrollees regardless of service use. H/T Blumberg and Cope. HSAs are a legislative response to a problem in health care economics that occurs when “consumer demand for health care responds to the reduced marginal cost of care to the individual”. As clarified by Mark Pauly in 1968: whenthe cost of the individual’s excess usage is spread over all payer-members of a group, the individual is not prompted to restrain her usage.
In direct primary care subscription medicine, there is a marginal cost of zero for every medical service the individual consumes. All demanded units of DPC covered medical services are paid by monthly fees collected from each member, regardless of that members service use.
That’s precisely opposite to the reason for HSAs.
The HSA tax break exists to get patient-consumers to commit to putting more “skin in the game” through a specified, high level of deductible; the legislative designers forbade participants from “taking skin out of the game”, i.e., from defeating the legislative purpose by taking a second “health plan” that reduces that commitment and the effective burden of that deductible. This is why it is perfectly clear that secondary coverage (e.g., as a dependent on a spouses plan) is HSA-disqualifying.
The undefined words “health plan that is not a high-deductible health plan” in the HSA legislation should be interpreted to include any health payment arrangement, including direct primary care, that lowers the burden of high deductibles and defeats the purpose of the legislation. IRS’s interpretive discretion does not extend to undermining the intent of HSA legislation.
DPC advocates’ brag about how “there’s never a deductible”, no how many covered primary care services a patient actually utilizes. That’s exactly why paying DPC fees is incompatible with the reason for HSAs.
A benefits attorney has opined that the IRS should allow DPC subscribers to use HSA because DPC “complements” high deductible insurance. But Congress seemingly intended a specific health care payment model for complementing the coverage provided by high deductible insurance coverage — high deductibles!
Direct Primary care clinicians and advocates often point out, accurately, that they serve a broad socio-economic range of patients. The range is well illustrated by a pair of oft-appearing themes, “concierge care for the middle class” and “affordable care for those who fall between the cracks”. In turn, the themes are reflected in two almost polar opposite insurance profiles for each of which DPC presents a solution: those in the middle class with sound, high-deductible insurance policies and those with low incomes for whom standard health insurance of any form is beyond their limited means.
The uninsured are not a tiny sliver of DPC subscribers. A recent survey put their numbers at about a quarter of DPC patients, and many DPC docs say 30-40% in their own practice. Indeed, a January NPR piece on the use of DPC by HDHP holders immediately prompted the DPC Alliance to vigorously advise the public that the economically disadvantaged are the “focus” of quite a few direct primary care practices.
The middle class HDHP group predominantly join DPC for mixed reasons of economy and concierge-like convenience, making a relatively good situation even better. Many of them — surely those with the most discretionary spending ability — are able to save. The low income uninsured on the other hand, enter DPC subscriptions to make the best of a bad situation, and they have essentially nothing to bank.
The Primary Care Enhancement Act and similar initiatives seek to provide substantial tax subsidies for direct primary care subscription fees, but these flow only to those who have BOTH high deductible health insurance plans AND enough spare income to facilitate actual savings accounts. But this kind of “fix” does less than nothing to those on the other side of the income/insurance divide; for them, the “fix” actually makes things worse.
Economics 101 teaches that government subsidies increase the price of the subsidized goods or services. The middle-class DPC members with insurance may, or may not, see net benefit from a subsidy; since the supply of family physicians is tight, most of the subsidy will probably flow to the providers as increased subscription fees. In any case, what low income DPC members will get from a “fix” is higher subscription fees.
Already priced out of standard insurance and forced into direct primary care, they will be pressed even harder. And some will find themselves forced out of direct primary care.
Subsidies for middle class savers (and/or their DPC physicians) may or may not be warranted by the purported virtues of direct primary care. But subsidies that are directed toward DPC’s financially soundest subscribers should not come at the cost of pushing DPC’s most financially desperate and loyal patients out of their best chance of quality care. Almost any other way of investing federal resources in DPC would be more fair and better targeted.
Do no harm.
But wait, there’s more.
Bonus # 1: The DPC/HDHP/HSA fix aggravates an income inequity among the insured population that is already baked into the DPC cake.
The signal feature of the DPC world is that direct primary care clinics do not take insurance, so entry is overwhelmingly on a cash only basis. DPC is effectively unavailable to anyone who is insured but does not have the financial resources to buy an additional layer of primary care services that neither draws insurance reimbursment not get credited against a deductible. By the same considerations, DPC becomes increasingly available as the income ladder is ascended.
When that socioeconomic reality is coupled to DPC emphasis on small patient panels and easy access, the resemblance of DPC to concierge medicine undercuts any argument for relaxing HSA rules on DPC. In fact, the HSA break amounts to a regressive subsidy that supplements funds being spent on DPC; this has the effect of growing the rate at which DPC becomes increasingly available as the income ladder is ascended. An HSA break brings DPC closer to concierge care.
Bonus # 2: the DPC/HSA fix aggravates the rural health care provider shortage
DPC advocate claims of being all things to all men sometimes take the form of, “DPC is the best hope for primary care in rural areas.” But the effect of a DPC/HSA fix will be to drive DPC physicians toward areas where middle class HDHP savers are in large proportion and away from rural areas where there are plenty of the poor and disproportionately few in the middle class.
Identifying DPC nonsense does not require a law degree.
Watch out. Near you is a direct primary care advocate begging a legislator or regulator to make his medical practice less accountable. He is stomping his feet very, very hard and he’s shouting “This is not insurance”, “There is no risk being transferred”, or “My practice is not a ‘risk-bearing’ entity”.
This is not rocket science.
Question Set #1:
A. The day before I enter into a direct primary care contract, am I at risk of falling while out on a walk, spraining my ankle, and wounding myself by falling on a sharp object?
B. The day after I enter into a direct primary care contract, am I at risk of falling while out on a walk, spraining my ankle, and wounding myself by falling on a sharp object?
Answer Set #1
Question Set #2
The day before I enter into a direct primary care contract, am I at financial risk of having to bear the costs of primary care services needed to treat the consequences of my falling while out on a walk, spraining my ankle, and wounding myself by falling on a sharp object?
The day before I enter into a direct primary care contract, is any direct primary care physician at financial risk of having to bear the costs of primary care services needed to treat the consequences of my falling while out on a walk, spraining my ankle, and wounding myself by falling on a sharp object?
Answer Set #2
Question Set #3
A. The day after I enter into a direct primary care contract, am I at financial risk of having to bear the costs of primary care services needed to treat the consequences of my falling while out on a walk, spraining my ankle, and wounding myself by falling on a sharp object?
B. The day after I enter into a direct primary care contract, is my direct primary care physician at financial risk of having to bear the costs of primary care services needed to treat the consequences of my falling while out on a walk, spraining my ankle, and wounding myself by falling on a sharp object?
Answer Set #3
Question Set #4
A. If on the day before I entered into a direct primary care contract, I was the one at financial risk of having to bear the costs of primary care services needed to treat the consequences of my falling while out on a walk, spraining my ankle, and wounding myself by falling on a sharp object and on the day after I entered into a direct primary care contract, my direct primary care physician was the one at financial risk of having to bear the costs of primary care services needed to treat the consequences of my falling while out on a walk, spraining my ankle, and wounding myself by falling on a sharp object, had there been a transfer of the financial risk of having to bear the costs of primary care services needed to treat the consequences of my falling while out on a walk, spraining my ankle, and wounding myself by falling on a sharp object from me to my direct primary care physician?
B. How many heads of direct primary care advocates just exploded?
Answer Set #4
The State of New York has the financial capital of the world, has the most insurance companies in the country, and was the biggest state for the longest time. For these reasons is generally looked to for leadership in the law on financial subjects primarily governed by state law. Here’s how they define an insurance contract.
(a) In this article: (1) “Insurance contract” means any agreement or other transaction whereby one party, the “insurer”, is obligated to confer benefit of pecuniary value upon another party, the “insured” or “beneficiary”, dependent upon the happening of a fortuitous event in which the insured or beneficiary has, or is expected to have at the time of such happening, a material interest which will be adversely affected by the happening of such event.
(2) “Fortuitous event” means any occurrence or failure to occur which is, or is assumed by the parties to be, to a substantial extent beyond the control of either party.
Every other state has the same core idea of an obligation dependent on a fortuity.
Bonus round (advanced players only).
A. A municipality in the western corner of South Carolina self-insures to cover the costs of its employees’ health care. To meet part of its commitment to its employees it engages a group of primary care physicians who call themselves Western South Carolina Capitated Access MD. The employer pays them a fixed monthly fee for each employee who wishes to be a clinic patient in exchange for as much primary care as each such employee may need during the covered period. Is that capitation?
B. A municipality in the western corner of South Carolina self-insures to cover the costs of its employees’ health care. To meet part of its commitment to its employees it engages a group of primary care physicians who call themselves Western South Carolina Direct Access MD). The employer pays them a fixed monthly fee for each employee who wishes to be a clinic patient in exchange for as much primary care as each such employee may need during the covered period. Is that capitation?
For pretzel lovers: For DPC advocates’ views on risk, capitation, and direct primary care, see this tweet thread and this one. For more DPC advocate double talk in this genre, extended to address adverse selection, try this masterpiece. See discussion here.
The direct primary care community has long tried to support claims that DPC reduces overall health care costs by 20% to 40% with non-risk-adjusted cost-reduction data drawn from employment health plans that allowed employees to elect between DPC and FFS primary care options options. But the first and, so far, only time that independent, neutral, certified professional actuaries looked hard at such a program, careful risk-adjustment showed that the savings claimed were merely an artifact of heavy selection bias. A DPC poster child, the Union County employer program — previously lauded for its claimed 28% cost reduction — was shown by Milliman Actuaries to have had a DPC cohort so young and healthy that it would have been predicted to incur on average 36% less in health care costs than its FFS counterpart.
Any reasonably informed employer or policy maker facing claims about the cost-effectiveness of direct primary care should insist that DPC provider boasts be scrutinized for evidence of selection bias.
In my last post, I noted that the DPC advocacy community has not even bothered to address the simplest of selection bias indicators, the younger average age of DPC cohort members compared to their FFS counterparts. I also noted that Milliman Actuaries have an age/gender risk-adjustment model1 and considered using it for the Union County study.
I further noted that in its Union County study, Milliman relied on a more complex model (“MARA-Rx”) which consider age, gender, and various therapeutic classes of prescription medications used by DPC and FFS group members. Milliman’s Rx risk-adjustment methodology, like its other risk adjustment models, was developed and validated as a health care cost predictor using health care cost data for millions of patients. There is nothing I can see, and nothing in the literature, to suggest that the Rx model has any inherent features that would unfairly penalize a DPC cohort in a studied DPC option employer health plan. As yet, no one in the DPC community has objected to Milliman’s use of the Rx methodology to assess the Union County DPC program, or to its future use in evaluating any other similar program.
There are even more complex and expensive methodologies, like MARA-Cx, that add diagnostic data harvested from payment claims to the factors used in MARA-Rx. In the Part 1 post, I also mentioned an arguably surprising difference in approach to selection among risk-adjustment methodology between Milliman Actuaries, who have no financial interest in the direct primary care movement, and KPI Ninja, a data-analytics group closely connected to the direct primary care movement. Both Milliman and KPI Ninja concurred that risk adjustment methodologies like “Cx” are likely to understate risk score for DPC cohort members because direct primary care physicians do not file claims.
KPI Ninja pointedly laments this “donut hole” in the claims-based data. But there is no anti-DPC donut hole in Rx based risk adjustment methodology.2
Although it possesses a fully-validated claims-based risk adjustment methodology (“MARA-Cx”), Milliman’s common-sense response to the data donut hole problem was to set that Cx methodology aside and determine risk-scores for the Union Count cohorts using only the Rx age/gender/prescription drug methodology. Like Milliman, KPI Ninja has access to risk-adjustment software engines that have equivalents, in a package known as ACG®, to both Rx and Cx. Unlike Milliman, KPI Ninja seemingly rejects Rx methodology and, instead, embraces Cx type methodologies that have the very donut hole KPI Ninja laments.
Why complain about the donut hole in Cx, then reject Rx which has none, and then return to and embrace Cx? Might it be precisely because KPI Ninja knows that Rx based risk adjustment will produce results that are sound, but not happy, for its cherry-picking DPC clients? On the other hand, when convenient, a donut hole can first be performatively disparaged as biased, then filled with custom data products developed by KPI Ninja to tell stories more to DPC’s liking.
Context: DPC docs feel coding makes their patients sick.
Direct primary care practitioners avoid third-party health care payment of claims and the (often digital) paperwork that accompanies it. While the logic of subscription practice renders coding procedures for reimbursement unnecessary and, therefore, a target of scorn, D-PCPs also disdain the type of recording of diagnostic codes that attend claims for third-party reimbursement.
Here’s what Dr Jeff Gold, a co-founder of the Direct Primary Care Alliance, had to say in a post entitled, “ICD-10: It’s Nice Not Knowing You.”
This is nothing more than another layer of bureaucratic red-tape that does nothing to enhance the quality or cost of your care, but rather furthers the disease process. All it does is waste more of your physician’s and office staff’s time – time that should be spent working towards your care. . . .
DPC doctors, in other words, not only decline to file claims and code their procedures, they also hold industry standard diagnosis coding in fiery contempt. As a result, the donut hole problem can not be solved by simply collecting DPC patient diagnosis codes from their direct primary care physicians.
Enter KPI Ninja — with balm for DPC’s abstinence
Missing data reduces population risk score. Meaning it will look like you are treating healthier patients in the eyes of those who use these risk scores (employer data vendors, brokers, health plans) … aka they can argue that you are cherry picking.
While the details remain murky, KPI Ninja seemingly plans to meet cherry-picking charges by filling the donut hole with information somehow scoured from such EHR records as direct primary care doctors may have.
In theory, KPI Ninja could develop a way to reverse engineer a DPC’s EHR entries and other information to generate the diagnostic codes upon which the DPC physician would have arrived had she not thought that participation in diagnostic coding was wasteful. How proceeding “backwards” to arrive at codes would result in any less waste is difficult to image, so that effort strikes me as misguided.
In any event, validation of a reverse engineering model would likely require resources beyond those of KPI Ninja and the DPC advocacy community. It would also likely require the participation of a control group of D-PCPs willing to do extensive coding.
However, for physicians like Dr Gold who have identified ICD-10 coding as “furthering the disease process”, such participation in a coding control group would be an ethical violation; it would “do harm”. Furthermore, if Dr Gold is correct, the members of a patient panel studied in such an experiment would have to give informed consent.
Furthermore, to whatever extent EHR mining for diagnosis codes omitted by a D-PCP produces fully accurate risk codes for members of a DPC cohort, the same mining techniques should be applied to the EHRs of FFS patients to correct any omissions by FFS-PCPs. What’s sauce for a DPC goose is sauce for an FFS gander.
Finally, fair implementation of a model in which diagnosis codes for risk scoring are derived from DPC-EHRs for comparison with diagnosis codes from FFS-claims would require safeguards against the DPCs deliberately larding EHR’s with entries that result in up-coding, just as FFS-claims data is subject to procedures to check up-coding. Again, goose/gander.
Perhaps, KPI Ninja merely has in mind developing a direct method of converting mined EHR data into risk factors that are not directly commensurate with those from diagnosis-based risk models, but that are instead presented to inquiring employers and policy-makers as an alternative model of risk-adjustment.
Precautions would still apply, however. If EHR data on, say, biometrics or family history is brought in demonstrate that the DPC population is less healthy than average, a knowledgeable employer should insist on counterpart data from the EHRs of FFS patients.
A recent addition to KPI Ninja’s website suggests their emphasis may rest on pre-post claims comparisons. It will of course be important to include pre- and post- data on DPC patients and their FFS counterparts. That will be somewhat revealing if the pre-choice claims data for the two populations are similar. But if the results of the Milliman study are representative, that will most likely not be the case.
In the more likely event of a higher level of prior claims in the FFS population, any “difference in difference” analysis of pre-post claims between DPC and FFS populations will still require an attempt to see whether the FFS higher “pre” claims might be accounted for by intractable chronic conditions. Such a finding would impeach any inference that DPC group pre-post gains can simply be projected onto an FFS group. And such a finding seems likely, if the Milliman actuaries were correct to ascribe selection bias to the tendency of the ill to stick with their PCPs; a “sticky” relationship to a PCP seems quite likely to correlate “sticky” chronic health conditions that bind the patient and the PCP.
Further explication from KPI NInja as to how its plans will work was said to be forthcoming. I appreciate the insights they have given in the past, and look forward to learning from them in the future.
There are many risk adjustment software packages available from neutral academics, accountants, and actuaries. They can be expensive; even access to the packages supplied by CMS for use by insurers participating in the Affordable Care Act run a bit over $2 per enrollee per year. Importantly, some of the packages rely on proprietary algorithms, but nonetheless tend to be generally transparent. In most cases, however, these packages come from neutral academic or actuarial sources.
Per se, I fault DPI Ninja neither for its close connection to the DPC industry nor for offering to DPC clinicians the best “risk adjustment” bang that can be generated from the least record keeping buck. To the extent that DPI Ninja delivers DPC data that is generally transparent in its methods and assumptions, that work will speak for itself. Should DPI Ninja lay on data produced by secreted assumptions and methodology, however, it should expect that its business relationship to DPC will affect the credibility of that data.3
First, none of foregoing is intended to deny that membership in a direct primary casre practice can significantly reduce usage of emergency departments. Of course it can! The question is what does it cost to avoid such visits. It is very rare for United States Presidents to go to emergency rooms as there’s always a doctor within a few hundred feet. DPC docs are easier to reach at odd hours and are more available for same day visits. They have fewer patients, and that costs a lot more money per patient.
Second, I have discussed in many posts the many sources of selection bias. Use the search item in the menu to find example using words like “selection”, “bias”, or “cherry”. It’s real and the only truly debatable proposition is whether DPC advocates who deny it are unintentionally fooling themselves or deliberately fooling you.
1 A college freshman IT student could probably develop an age/sex risk adjustment engine and apply it age/sex data from paired DPC/FFS cohorts over a weekend. If DPC clinics don’t report age/sex/risk comparisons with their FFS counterparts, it’s because they know full what the results would show.
2 It may even be that Rx metholodogy favors DPCs.
I expect to hear soon that Rx risk adjustment discriminates in favor of “bad”, overprescribing PCPs by making their patients seem sicker than those of “good” doctors who never overprescribe. But this can become an argument that Rx discriminates against DPC if, and only if, it is also assumed that DPC has fewer “bad” overprescribers than does FFS. There is no clear factual basis for assuming, in essence, that DPC doctors are simply better doctors and prescribers than their FFS counterparts.
If anything, DPC self-promotion suggests that Rx data would be skewed, if at all, in the opposite direction. DPC advocates regularly claim that DPC avoids expensive downstream care by better discovering illness early and managing chronic conditions better, by coaching patient compliance and by lowering the cost of medications. Another recurrent theme among DPC advocates is that FFS doctors rely too heavily on specialists; but, if true, FFS cohort patients would be more likely that DPC patients to have over-prescription “wrung out” of their regimes. In these ways, applying a risk adjustment methodology based on prescription medication data, DPC’s own bragging suggests that any donut hole is likely to make the FFS cohort appear healthier.
3 The following fairly new statement on KPI NInja’s website does not bode well, suggesting both secrecy and a predetermination of an answer favorable to DPC.
“We analyze historical claims data from self-insured employers by utilizing our proprietary algorithms to identify cost savings and quality improvement opportunities. Get in touch to learn more about how we can help show your value to employers!”
Within days of the Milliman report warning of the “imperative to control for patient selection in DPC studies [lest] differences in cost due to underlying patient differences  be erroneously assigned as differences caused by DPC”, the first rumbling of resistance from the DPC advocacy community emerged. This was a suggestion, addressed to one of its authors, that the Milliman study may have treated the direct primary care employer unfairly.
KPI Ninja also reached out to me. After some initial misunderstanding on my part, and subsequent examination of KPI Ninja’s online published material on this subject, I reconstruct my understanding of KPI Ninja’s argument in the immediately upcoming section. For reasons discussed in the next following section, I have concluded that KPI Ninja’s argument, although not without insight, simply does not apply to the risk-adjustment methodology used in the Milliman study itself. Thereafter in this post, I begin to respond more broadly to the KIP Ninja critique and to the not-yet fully visible “remedy” it is apparently developing. I will conclude in a subsequent post.
KPI Ninja’s Donut Hole Concept
Some data used in some risk-adjustment methodologies are diagnostic data harvested from claims, including both primary care and downstream care claims. The risk that manifests itself in downstream claims properly counts in evaluating a patient’s risk, whether the patient is a DPC or an FFS patient. But a problem can arise (a”donut hole” in KPI Ninja’s lexicon), in situations where some PCPs have success at averting downstream claims. If the patient is in an FFS practice, the practice is scored as bearing any risk factor appearing in the primary care claims submitted by the FFS PCP. FFS practices, in essence, get fair credit for their good work in avoiding downstream claims. On the other hand, D- PCPs do not file primary care claims; as a result DPC patients risk factors go unrecorded when not reflected in downstream claims; DPC docs therefore do not get proper credit for their good works.
In the upshot, DPC patients look relatively less risky than they really are. On this analysis, risk-adjustment by claims dependent methods of two equally risky panels of DPC and FFS patients will inevitably disfavor DPC.1
The Milliman study of DPC used risk-adjusment that had no donut hole.
There are ways of evaluating population risk that do not depend on harvesting diagnostic data from physician claims, methods for which there is no claims associated donut hole. Milliman itself, based on experience with millions of people, developed and validated at least two methodologies that do not require claims data: one (age/sex) determines risk factors based only on the age and gender characteristics of the populations being compared; a second (Rx) adds, to age and gender, additional patient information about the usage of different therapeutic classes of prescription drugs. Under the Rx methodology, claims are not looked at; DPC and FFS docs get the same “null credit” for every prescription drug avoided through their primary care work.
Of course, Milliman also has a claims based risk-adjustment methodology (Cx). For its study of Union County’s direct primary care option, Milliman carefully considered using the Cx methodology and, precisely because they identified the donut hole, expressly rejected it. Milliman also considered using its age/gender risk adjustment methodology , but decided to use its more granular RX methodology.2
Simple, lost cost risk-adjustments are affordable for modestly sized employers
The beauty of an age/gender risk-adjustment is that it is straightforward. Nor is it doomed to failure by its simplicity. Over three-fourth of persons under the age of 65 lack any diagnosis consider significant for risk adjustment carried out under the Affordable Care Act. Nor is it likely to be expensive; it is definitely not rocket science. A college freshman just learning how to use a spreadsheet could fire it off in an hour, if pointed to a readily available table, and a data set with each employee’s age and gender.3
In its own published “research”, one of the larger DPC groups (Nextera) used raw total claims from an immediate pre-DPC period as a basic risk adjustment methodology for employer DPC and non-DPC groups claims data for the period following the creation of the DPC option. Those choosing Nextera had vastly lower prior claims.4
Dan Malin, a TPA exclusively serving small employers, addressed a 2019 DPC Summit forum on employer DPC with about 100 attendees present. He claimed that his firm could calculate the medical consumption of that group for a coming year to within five or six percent by having each fill out an ordinary health insurance application.
Has an employer-option DPC cohort ever been OLDER than its FFS counterpart?
Or ever entered a DPC option with a HIGHER prior claims experience?
But DPC advocates could go some of the way long way toward reassuring ordinary employers by demonstrating measurable similarities between cohort members being studied. Say, for example, that a DPC group had virtually the same claims as an FFS group in a claims year prior to creation of a DPC option. Unfortunately, for some reason, whenever these type of comparison have been made, the DPC cohort has turned out to to have had a history of smaller pre-DPC claims. This might be because, as Milliman warned, direct primary care is a cherry-picking machine.
As one DPC thought leader has pointed out, “larger direct primary care companies like Qliance, Paladina, and Nextera have repeatedly reported 20% plus savings for employers using the DPC model. Many smaller employers have found similar savings .” If those selecting DPC were equally likely to be older or younger than their FFS counterparts, the chance that even as few as ten5 such studies would each have had younger DPC populations would be less than 0.1%.
Alas, for some reason, there are no known reports of a DPC cohort being older than its FFS counterpart. Also, perhaps for some related reason, it often appears that employer DPC cohorts are younger than their FFS counterparts.
In some cases, DPC advocates make an effort to show that a presumably large percentage of a DPC cohort have multiple chronic conditions, but reliably matched data from the corresponding FFS counterpart cohort has not, to our knowledge, been reported. Indeed, a DPC advocate promoting the very poster-child clinic studied by Milliman once sarcastically dismissed the idea of cherry-picking by pointing to the chronic conditions of the DPC’s patients — while noting that “control group data is not available”. Really! Since Milliman presented that very control group data in May, we’ve heard no comment by that author.
Does any of these methods have any significant built in distortion against direct primary care? Does direct primary care make younger? Did receiving direct primary care in one year lower claims costs for the preceding year?
1 A masterful extention of that argument came in a tweet from one DPC thought leader, who was asked about the lack of risk-adjustment in a just-published savings claim for his own DPC practice. He invited the inquirer to consult DPI Ninja “or spend $100,000 to prove them right or wrong.” Is this how he plans to answer employers who’ve read Milliman’s advice for employers?
2 Interestingly, Milliman recorded that adjustment using Rx in this particular case was more favorable for DPC than using age-sex adjustment.
3 Virtually every direct primary care clinic in the country has chosen an even simpler path when using age only to establish a variable, usage-anticipation- component for its monthly fee DPC schedule.
4 The employees who chose Nextera’s direct care had, on average, historical prior claims that were 30% less than those who declined direct primary care. Interestingly, Nextera sought to rely on a “difference in difference” analysis, but intertemporal claim comparisons of that sort fail if, for example, the declining groups higher “pre” claims can be accounted for by intractable chronic conditions, something that seems entirely likely.
5 At the moment of commencement of DPC Summit 2020, a least ten such studies of employer option DPC have been publicized. At least one additional study seems likely to appear on July 18, 2020. I wonder if the study results reported will include a comparison of the average age of the two cohorts.
An actuarial study brings employer direct primary care to a turning point.
Milliman’s actuaries insisted that DPC cost reduction data without risk adjustment is essentially worthless. A second prong of Milliman’s analysis suggested that the direct primary care model is associated with a 12.6% over-all reduction in health services utilization*. Then, working from that number, Milliman went on toward a third suggestion: that an employer who buys into DPC at an average price of $60 PMPM would likely have an ROI of zero.
That is not a very good deal for either an employer or a DPC practice.
For an employer, entering into a break even deal would mean foregoing other investment opportunities, while incurring inconvenience of change and probably a loss of good will from a number of employees who might be, or feel, forced or financially pressured into a narrow primary care network. Why bother?
For a DPC provider, $60 PMPM works out to $432,000 per year for the patient panel of 600 members that DPCs like to brag about. But an average PCP compensation package in, say, Anderson, South Carolina comes to about $276,000. That leaves about 36% of revenue for all overhead. That’s not much.
The American Academy of Family Physicians tells us that overhead for family physician practices runs around 60% of revenue. To get nearly down to 36%, DPC docs turn to their built in savings on billing and insurance costs, but that seems likely to fall a bit short of the needed reduction of 24% of revenue. The most recent peer reviewed study of the subject (2018) indicated billing and insurance (BIL) costs for primary care came out to about $20 per visit; for a PCP with the AAFP-reported average visit time of 24 minutes (and having 20 visits a day, 5 days a week, 50 weeks a year) that’s $100,000 — and still only about 23% of revenue. A somewhat earlier peer reviewed study came nowhere near this, finding that physician practices had BIL costs of about 13% of revenue.
In the direct primary care employer clinic, moreover, billing costs do not fall to zero; patient rosters need to be kept up to date, matched to employer records, and processed. In addition, most direct primary care physicians also provide a significant measure of separately paid goods and services for which an employee, employer, employer’s insurer, or a TPA may require documentation and billing. Moreover, much of the data attributed to billing and insurance costs in an FFS setting has a counterpart in direct primary care collection of metrics needed to demonstrate value to an employer.
Accordingly, even for a non-insurance direct primary care clinic, overhead of 36% of revenue is scanty.
At the same time, DPC practitioners have — and regularly express — a very high opinion of themselves and the care they give. As group, they seem distinctly unlikely to settle for merely average levels of compensation.
The most profitable path forward for direct primary care is to persuade employers that paying PMPMs over $60 will do more than break even. Historical brags, based on data that was not adjusted for risk, claimed employer savings from 20% to 40%. A DPC thought leader recently published a book on employer DPC that collected references to such seven studies, including one that claimed to have reduced employer costs by 68%.
Now that real actuaries have weighed in, those days are numbered.
* I believe that Milliman’s 12.6% figure vastly overestimates the reduction in health services utilization association with direct primary care. As explained here, DPC may even result in an increase in health services utilization. Is it really plausible that taking a scarce resource — the time of PCPs — and spreading it thickly over tiny patient panels would NOT result in net economic loss?
Do you remember when Union County’s three year DPC commitment for 2016-2018 was claimed to be saving Union County $1.25 Million per year? So why did Union County’s health benefits expenditure rise twice as fast as can be explained by the combined effect of medical price inflation* and workforce growth?
For the first year or two, a clinic owner in an employer DPC option may get away with presenting the employer with data brags that package selection bias artifact as DPC cost-effectiveness. The wiser employers will figure selection bias out before making the mistake of tossing all their employees into direct primary care based on non-risk-adjusted data.
For those employers who do not figure this out, it will be interesting to watch the DPC clinics adapt to the influx from the sicker, older population. Better, it will be outright fun to hear DPC clinic owners explain why having more employees in their clinic led to an increase in PMPMs for DPC patients. Since it will be hard for them to suddenly come to Jesus on the cherry-picking issue, where will they turn? Probably to blaming a combination of Covid-19, insurance companies, and Obamacare.
*For medical cost inflation I used the Bureau of Labor Standards medical goods and services marketplace statistics. I thank economics student D.S. for pointing out that in the employer insurance sub-market the rate of increase was higher. Kaiser Family Foundation’s annual Employer Heath Benefits Survey data indicate that for the period 2015-2018, costs increased 11%. Substituting 11% from KFF for the 8.5% from BLS, I compute that the 47% increase in Union County was not 200% of that expected, it was a mere 175% of that expected.
Of course, I might have attempted a narrower focus. I fully realize that the average increase might have been different for North Carolina employers, or for county employers, or for county employers in North Carolina. Then, too, there are a total of counties named Union in a total of 17 states. I confess that I deliberately refused to determine the employer health coverage cost inflation rate for the other 16 Union Counties. Life is short.
It might also be the case that bringing DPC to Union County created such great opportunities for affordable health care that DPC employees married at a higher rate, and decided to have more children. And, of course, the presence of DPC might have helped Union County recruit a large tranche of new employees with enormous families.
In 2016, the share of people between 65 and 74 who were still working was over 25%. Any of them working at employers with more than twenty employees covered by group health plans are required by law to be included in the employer’s plan. They may also enroll in Medicare Part B. Some employer plans even require their Medicare-eligible employees to enroll in Part B. When optional for the employee, the choice to add Medicare Part B and have dual coverage is typically made by relatively heavy utilizers looking to meet cost-sharing burdens by having Medicare as a secondary payer.
In any event, elder employees with dual coverage will have low, or even zero, out-of-pocket expenses, whether for primary care visit fees; for the types of services, like basic labs, often included in an employer DPC package; or for downstream care. These elders have relatively little incentive to join DPCs, especially in cases where the employee pays more for a DPC option than for a non-DPC option (such as Strada Healthcare’s plan for Burton Plumbing).
Many with dual coverage will have even more incentive to avoid DPC. A large majority of DPCs, including DirectAccessMD, Strada Healthcare, and many Nextera-branded clincs, have opted out of Medicare. Medicare-covered employees who receive ancillary services that the DPC performs for separate charge will be expected to see that the DPC gets paid, but will receive no Medicare payment for those services. A Medicare-covered employee in Nextera’s St Vrain Valley School District plan, for example, would be denied the ability to have Medicare pick up his cost-share for Nextera’s in-office labs and immunizations, Nextera’s on-site pharmacy, or Nextera’s on-site cabinet of durable medical equipment. Were a dual covered employee to choose the Nextera clinic, she would have to make a point of declining to have Nextera draw her blood work or put her in a walking boot.
Most employer workforces have a relatively small percentage of employees over age 64. But provider health coverage for these elders is apt to be very costly. The employees likely to be most costly are the very ones that will find Medicare Part B’s annual premium of less $1750 a good bet for avoiding cost-sharing burdens like those in Nextera’s SVVSD plan – a $2000 deductible and a $4000 mOOP.
Accordingly, those with dual coverage are likely to be high utilizers of services with nothing to gain from DPC. Or, worse: some will pay more in employee contributions; some will have added costs and/or inconvenience owing to Medicare opt-out by the DPC provider.
These high-cost, dually-covered employees will disproportionately end up in the non-DPC cohort under most employer DPC option plans. And every one of them will skew non-risk-adjusted claims data, contributing to a selection bias artifact masquerading as DPC savings.
Much the same reasoning will apply to other employees who have a secondary coverage, such as being a covered spouse. Dual coverage usually comes at a price, such as a premium add-on for spousal coverage. But the price will often be worth it for high utilizers whose primary coverage has high cost sharing burdens that can be brought to negligible levels. For these high utilizers, the incentives to select a DPC option are minimal, even negative if the DPC option comes with a larger employee contribution.
Finally, whatever the source of secondary coverage, the heavy utilizers for whom it is particularly desirable are also the very people most likely to cling to particular PCPs who have served them well in the past, rather than sign on with a DPC clinic offering a narrow choice.
Two recent DPC brags fit together in a telling way.
Nextera Healthcare reported non-risk-adjusted claims data indicating that employees of a Colorado school district who selected Nextera’s DPC option had total costs that were 30% lower than those who selected a traditional insurance option. But that employer’s benefit package confers huge cash advantages (up to $1787) on risky, older patients if they choose the non-DPC option.
Comes now DirectAccessMD with a report of non-risk-adjusted claims data that employees of a South Carolina county who select a DPC option have percentage cost “savings” that are less than half of those shown for Nextera, a mere 14% lower than those who selected a traditional insurance option. But that employer’s benefit package design has an opposite effect to Nextera’s, conferring a significant cash advantages on risky, older patients if they choose DPC option. (And, good on the County and the DPC for it!)
Nextera’s program works to push risky, older patients away from DPC; DirectAccessMD’s program welcomes them. Pushing the sick and old away helps boost Nextera’s corporate brag to more than double that of DirectAccessMD who, proudly, invite the same people in — even if it makes them appear less successful.
Even without a fancy actuarial analysis, or even a basic one based on patient demographics, it’s apparent that most of Nextera’s brag is merely selection bias artifact masquerading as Nextera DPC cost-effectiveness.
Is DirectAccessMD’s clinic free of selection bias? Not at all. Selection bias can also arise when older and/or riskier patients make enrollment decisions based on differences in access to physicians of their choice. Older, riskier patients tend to cling to a long-standing PCP rather than select from the relative few offered by DirectAccessMD. Think of this as a primary care form of cherry-picking by narrow-networking .
We have learned that Anderson County’s DPC patients are 2.7 years younger than their non-DPC counterparts. Applying CMS risk adjustment coefficients for age and sex reduces DirectAccessMD’s “savings” from 14% to 2% .
If you would like to bet that the age difference between the two Nextera school groups discussed above is less than 2.7 years, please use the contact form. I’ll be right there.
The DirectAccessMD clinic that serves the employees of Anderson County, SC, is run by a tireless advocate for, and deep believer in DPC, Dr J Shane Purcell. Here the employer, with Dr Purcell’s apparent support, has taken steps that seems to have somewhat mitigated the selection bias that is baked into most other direct primary care option arrangements. Specifically, the dual benefit plans here have both a lower deductible ($250) and a lower co-insurance maximum ($1250) for DPC patients than for non-DPC patients ($500, $2500). Where other benefit plans structures, like the Nextera SVVSD plan reported here, push higher risk patients away, the Anderson County plan is more welcoming to those patients. I applaud the County and Dr. Purcell.
In fact, a high risk Anderson employee can see more than $1500 per year in added costs if she declines DirectAccessMD. A patient expecting the average utilization seen for the FFS cohort (~$4750) in Anderson County would likely incur about $375 in added costs by declining DPC, where an average patient in Nextera’s SVVSD plan would have saved $925 for doing the same. Again, this important difference is a feather in Dr Purcell’s cap.
In November 2020, we applied CMS’s actuarial value calculator to compare the County’s plans. The traditional plan had an actuarial value of 82%, the DPC plan of 87%. More of this update below.
Yet, as the recent Milliman study suggests, high risk patients may be reluctant to disrupt standing relationships with their PCPs, and may choose to resist other incentives if it means having to select a new PCP from a small panel at a given DPC clinic. Consider also that older employees, even those not at high risk, are more likely than younger employees both to have deeper attachments to their long-standing PCP and to have more disposable income to spend on keeping that relationship going. On average, therefore, we would expect employees who eschewed the direct primary care package to be an older and/or riskier group. Let’s go to the tape.
Not surprisingly, raw data — without any risk adjustment — from the employer indicates a noticeably smaller percentage of purported savings than has been bragged about by other DPCs in the past. Anderson County’s net cost for DPC members came in at 9% less than for non-DPC members, but the employees in DPC paid OOP only about half of what their non-DPC counterparts did. Combining both employer and employee costs, the average total spend for Anderson County DPC patients came to about 14% less than for non-DPC patients, purported savings of $56 a month.
But note these warning from the Milliman study: “We urge readers to use caution when reviewing analyses of DPC outcomes that do not explicitly account for differences in population demographics and health status and do not make use of appropriate methodologies.” Or this more recent one: “It is imperative to control for patient selection in DPC studies; otherwise, differences in cost due to underlying patient differences may be erroneously assigned as differences caused by DPC.”
An risk analysis of the health status of all the county’s patients, fully detailed as to all chronic coniditions, may not have been financially feasible for a modest operation like Dr Purcell’s. But a sensible population demographic methodology is at hand: comparing the ages of the two populations and using that as a predictor of utilization. This is certainly a “rough approximation”. But, not only is a rough risk adjustment likely to be better than no risk adjustment at all, the reasonableness of using age as a proxy for predicted utilization is affirmed by the fact that nearly all DPC practices use age-cost bands, and no other risk-based factor, in setting their subscription rates. Basic demographics are at the core of risk adjustments used by CMS for the ACA; over 75% of ACA enrollees in insurance plans under 65 have no adjustment-worthy chronic conditions; they are risk-adjusted on demographics alone.
The coefficients for age/sex risk adjustment used by CMS for ACA plans in 2020 can be seen here. Dr. Purcell’s slide pointing out the DPC cohort of Anderson County employees was 2.7 years younger than the traditional cohort is here. Going back to the tape, I estimated the risk adjusted overall medical costs for the DPC membership to be about 7.1
% lower than for traditional primary care group.
A second adjustment points the other way. All other things being equal, richer plans are known to produce “induced utilization”. CMS’s risk transfer machinery applies an induced utilization factor to adjust for benefit richness. As shown on the calculation sheet also linked above, this adjustment would increase employer costs by 3.8%.
This brings a tentative measure of savings, pending more definitive risk adjustment, to about 10 % overall, about $40 PMPM.
Maybe it is not what what Dr. Purcell hoped, but his results are more promising than most others.
The great caveat, of course, is that proper risk adjustment could turn this estimate up or down. In the Milliman study, the difference between actuarial of the benefit packages of the DPC and FFS programs was modest, yet the Milliman team still found massive adverse selection into the FFS. Milliman accounted for this as the result of sicker people clinging to the trusted PCPs who had served them in the past. I think of that as adverse selection by narrow primary care panel. Whatever the explanation, Milliman found that the selection bias required an overall upward adjustment of 36% of DPC costs; and they predicted that most employer DPC option clinics would see similar. On the other hand, a fairly large difference in benefit packages favoring DPC members, as in Anderson County, is something the Milliman team appears not to have contemplated, and it must surely drive some of the risky into the DPC pool.
I am still not betting on DPC saving big money. But, if you call me with your proposed wager, I’ve shortened the odds.
In a prior post, I suggested that Milliman’s use of downstream claims data in assessing utilization in Union County’s employee health plans may have been distorted in favor of DPC because that downstream data had not been adjusted to reflect the effects of the County’s cost-sharing design on utilization.
In a footnote to a recent comment on a Milliman web forum, two Milliman actuaries addressed similar concerns for the first time.
In addition to increasing an employer’s share of costs, benefit changes can also affect how much care members utilize. This affect (sic) is commonly referred to by actuaries as induced utilization, and should be considered by employers when structuring DPC options and by those evaluating the impacts of DPC. For our case study, the benefit design under the DPC option was slightly richer in aggregate than the PPO option, meaning that based on benefit design differences alone, members would be expected to use slightly more services in aggregate when enrolled in the DPC option than when enrolled in the PPO option. This is due to the employer waiving cost sharing for primary care services as well as the medical deductible for all services under the DPC option. Since the difference was relatively small, we conservatively did not apply an induced utilization adjustment in our estimates. If we had, the reduction in demand corresponding to enrollment in the DPC option would have been slightly greater.[Emphasis supplied.]
I thank Milliman for putting a proper actuarial name on my concerns. I am not an actuary.
When I first saw this, I took issue, suggesting that the details of the small difference might matter if the whole thing was looked at with sufficient granularity.
I since learned how to deploy the CMS AV calculator and CMS rules on induced utilization adjustment for risk transfer to get the granularity I sought.
Silly me. They were right. In fact, the induced utilization adjustment would be about about 0.2% in favor of the DPC.
If you want an example of induced utilization, consider the Nextera SVVSD clinic — where the non-DPC cohorts gets a $750 HRA unavailable to DPC members, while paying only 10% co insurance versus 20% for the DPC members. This not only creates a huge selection bias that pushes the sick away from DPC, it drops healthy family members who were just brought along into a very favorable space for health care consumption.
At last, it dawns on me. Selection bias is baked into virtually every DPC cake.*
Direct primary care usually comes with a significant price and a package of financial incentives revolving around primary care (and, sometimes, around some downstream care). For some, the game may be worth the candle. The incentives, typically the absence of primary care visit cost-sharing and free basic labs and generic drugs, have their best value for those who expect total claims to fall near but still short of their deductibles. These people are relatively low risk.
For those expecting to have total claims that will exceed their deductibles even if they receive the incentives, the dollar value of those incentives is sharply reduced — usually to a coinsurance percentage of the claims value of the incentive. These people have risks levels that range run from a bit below average risk to well above average risk.
The least healthy people have the highest claims. At a next level, and all the way up to the stratosphere, are insured patients expecting to hit their mOOP in an upcoming year. For a typical employer contract, however, these people are not necessarily extreme; for an employee with a $2000 deductible and a $4000 mOOP, this represents a $12,000 claims year. That’s not even a single knee replacement at the lowest cash price surgery center. For these, DPC’s financial incentives have essentially zero financial value.
Higher risk patients have significantly less incentives to elect direct primary care. DPC patient panels are enriched for low risk patients while higher risk patients tend to go elsewhere.
Financial considerations apart, the higher risk patients are also likely to be the ones least interested in replacing established relationship with particular PCPs with primary care from a narrow panel DPC practice. A second reason why DPC patient panels are enriched for low risk patients while higher risk patients tend to go elsewhere.
The upshot: Virtually any employer-option DPC clinic can trot out unadjusted claims data that shows employers having lower PMPMs for DPC patients than for FFS patients. After risk adjustment, however, not so much.
*I recently came across an employer health benefit system that included both a DPC option and cost-sharing features that apparently mitigated selection bias somewhat. But note, in that program, employees who chose to retain relationships with PCPs not affiliated with the DPC clinic paid up to $1250 per year for that privilege. Layouts of that order seem likely to correlate with either profound health impairments or advanced age. I have learned that the non-DPC population at that employer is, on average, two years older than the DPC population. On a standard age-cost curve of ~4.6 to 1, every penny of the difference between the groups can be full accounted for.
Skillful actuarial work on risk adjustment. A clear warning against relying on studies that ignored risk adjustment. Implicit repudiation of a decade of unfounded brags.
An admirable idea on “isolating the impact of DPC model” from the bad decisions of a studied employer.
Milliman should have recognized that the health service resources that go into providing direct primary are vastly more than $8 PMPM that emerged from its modeling and should have done more to subject the data on which the number rested to some kind of validation.
Upshot: there is still no solid evidence that direct primary care results in a reduced overall level of utilization of health care services. Milliman’s report needs to clearly reflect that.
Overview: A core truth, and a consequence.
The Milliman report on the direct primary care option in Union County has significant truth, an interesting but unperfected idea, and staggering error. The core truth lay in Milliman determining through standard actuarial risk adjustment that huge selection effects, rather than the wonders of direct primary care, accounted for a 36% difference in total health care costs between DPC and FFS populations. Both Union County and the DPC provider known as Paladina Health had publicly and loudly touted cost differences of up to 28% overall as proof that DPC can save employers money. But naysayers, including me, were proven fully correct about Union County — and about a raft of other DPC boasts that lacked risk adjustment, like those regarding Qliance.1
The estimated selection pattern in our case study emphasizes the need for any analysis of cost and utilization outcomes for DPC programs to account for the health status and demographics of the DPC population relative to a control group or benchmark population. Without appropriate consideration for how differences in underlying health status affect observed claim costs and utilization patterns, analyses could attribute certain outcomes to DPC inappropriately. We urge readers to use caution when reviewing analyses of DPC outcomes that do not explicitly account for differences in population demographics and health status and do not make use of appropriate methodologies
Still, Union County had made some choices that made the overall results worse than they needed to be. That’s where Milliman’s ingenuity came into play in what might be seen as an attempt to turn the County’s lemon into lemonade for the direct primary care industry. And that is where Milliman failed in two a major ways more than important enough to make the lemonade deeply unpalatable.
The Union County DPC program was even more of a lemon than Milliman reported.
To Milliman’s credit, it did manage to reach and announce the inescapable conclusion that Union County had increased its overall health care expenditure by implementing the direct primary care option. Even then, however, Milliman vastly understated the actual loss. That’s because its employer ROI calculation rested on an estimate of $61 as the average monthly direct primary care fee paid by Union County to Paladina Health; the actual average month fee paid was $95. There had been no need for a monthly fee estimate as the fees were a matter of public record.
Though $1.25 millions in annual savings had been claimed, the total annual loss by Union County was over $400,000. Though 28% savings had once been bragged, the County’s ROI was actually a negative 8%. Milliman’s reliance on an estimate of the fees received from the County, rather than the actual fees collected, made a fiscal disaster appear to be close to a break even proposition.
Milliman’s choice likely spared the county’s executive from some embarrassment.
For more detail on Union County’s negative ROI and Milliman’s understatements of it, click here and/or here.
See here: I’ve changed the grade. Milliman’s came up with a sound idea, but flubbed the design details execution.
To prevent the County’s specific bad choices about cost-sharing from biasing impressions of the DPC model, Milliman developed a second approach that entailed looking only at a claim cost comparison between the two groups. According to Milliman, “this cost comparison attempts to isolate the impact of the DPC delivery model on the overall level of demand for health care services“. [Italics in original]. Milliman should be proud of thinking of this approach. It seems likely to work where It is feasible because both the DPC and FFS cohorts face near identical cost-sharing incentives for downstream care.
But that was not the case in Union County, where DPC and PPS patients faced very different cost-sharing regimes when making downstream care choices. The County’s cost-sharing choices are imprinted on the downstream claims that fuel the isolation model; for the average FFS patient cost-sharing discipline for downstream claims was much reduced relative to that experienced by the average DPC patient.
Until it completes the hard actuarial work needed to quantify the skew of the downstream claims cost data that fueled its isolation model, Milliman should back off its claim to have successfully determined the health care utilization impact of DPC.
For slightly more detail on why I’ve made the foregoing edits, click here.
The Milliman calculation of 12.6% overall savings turns on a a massive underestimate of the cost of the direct primary clinic studied.
Milliman needed to determine utilization for the DPC clinic.
Assuming, arguendo, that downstream claims data for Union County are not contaminated by the Union County cost-sharing schema would bring us to the next step. Milliman’s model for relative utilization is simple:
Milliman used claim costs for both downstream components of the computation, and for the FFS primary care utilization. But because DPC is paid for by a subscription fee, primary care utilization for the DPC patients cannot be determined from claims costs.
One reasonable way of estimating the health services used by DPC patients might be to use the market price of DPC subscriptions, about $61 PMPM. With this market value, the computation would have yielded a net utilization increase (i.e., increased costs) for DPC. Milliman eschewed that method.
Another reasonable way of estimating the health services used by DPC might be to estimate the costs of staffing and running a DPC clinic. Using readily available data about PCP salaries and primary care office overhead, probably this would come to at least $40 PMPM by a conservative estimate. With this low number, the computation would have yielded a net utilization decrease for DPC well under 5%. Milliman eschewed that method.
The lower the value used for utilization of direct primary care services, the more favorable DPC appears. Ignoring models that would have pointed to $61 and $40 values, Milliman used a methodology that produced an $ 8 PMPM as the value of the resources required to provide direct primary care. This resulted in a computed 12.6% reduction in overall usage.
But $8 PMPM is an absurdly low value. Just, try asking a few DPC providers what they would give you for eight dollars a month. Most will – rightly – regard it as an insult. The usual charge for adult care usually about 8 to 10 fold higher than that.
Milliman’s “ghost claims” method was ill-suited for DPC and vulnerable.
Milliman’s “solution”, however, turned on the stunning assumption that utilization of subscription-based holistic, integrative direct primary care could be accurately modeled using the same billing and coding technology used in fee for service medicine. As a group, the DPC community loudly disparages such coding both for failing to compensate their efforts and for wasting their time. D-PCPs don’t even use billing friendly EHRs.
Yet Milliman chose to rely on the clinic’s unwilling DPC physicians to have accurately coded all services delivered to patients, then used those codes to prepare “ghost claims” resembling those used for FFS payment adjudication, and then to have submitted the ghost claims to the employer’s TPA, not to prompt payment, but solely for reporting purposes. The collected ghost claims were turned into the direct primary care services utilization by application of the Union County FFS fee schedule. The result was $8 PMPM.
The $8 PMPM level of clinic utilization determined by the ghost claims was absurd.
Valuing the health services utilization for patients at the direct primary care clinic at a mere $8 PMPM is at war with a host of things that Milliman knew or should have known about the particular clinic it studied, knew or should have known about the costs of primary care, and knew or should have known about the nature of direct primary care. Clinic patients were reportedly receiving three visits a year; this requires more than $8PMPM ($96 PMPY). The length of clinic visits was stressed. County and clinic brag 24/7 access and same day appointments for 1000 clinic patients. The clinic was staffed at one PCP to 500 members; at $96 a year, clinic revenue would have been $48,000 per PCP. This does not pass the sniff test.
The only visible path to Milliman’s $8 PMPM figure for health services demand for the delivery of direct primary care is that the direct primary care physicians ghost claims were consistently underreported. About what one might expect from “ghost claims” prepared by code-hating D-PCPs with no motivation to accurately code or claim (or, perhaps, even with an opposite motivation). Milliman even knew that the coding habits of the DPC practitioners were inconsistent, in that the ghost claims sometimes contained diagnosis codes and sometimes did not. Report at page 56.
Yet, Milliman did nothing to validate the “ghost claims”.
Because the $8 PMPM is far too low, the 12.6% overall reduction figure is far too high. As noted above, substituting even a conservative estimate of the costs of putting a PCP into the field slashes 12.6% to something like 4%. If in place of the $8 PMPM , the $61 market price determined in the survey portion of the Milliman study is used, Milliman’s model would show that direct primary care increases the overall utilization of health services.
For more detail on the erroneous primary care data fed to Milliman’s isolation model, click here.
Union County paid $95 a month to have Paladina meet an average member’s demand. That Milliman computed the health care services demanded in providing DPC to be $8 per month is absurd.
Milliman should amend this study by adapting a credible method for estimating the level of health services utilized in delivering primary care at the DPC clinic.
Milliman’s good work on risk adjustment still warrants applause. Indeed, precisely because the risk adjustment piece was so important, the faulty work on utilization should be corrected, lest bad work tar good, and good work lend credibilty to bad work.
1 The reaction to Milliman’s making clear the necessity of risk adjustment by those who had long promoted the Qliance boasts was swift and predictable: DPC advocates never ignore what can be lied about and spun. DPC Coalition is a lobbying organization co-founded by Qliance; a co-founder of Qliance is currently president of DPC Coalition. DPC Coalition promptly held a legislative strategy briefing on the Milliman study at which the Executive Director ended the meeting by declaring that the Milliman study had validated the Qliance data.
If I were a direct primary care practitioner, I’d be mildly miffed at Milliman’s reducing what I do to a series of CPT codes. I’d be more worried by Milliman’s team setting the value of my health care services at $8 PMPM.
The $8 PMPM figure Milliman declared as the health care service utilization to deliver primary care to DPC patients was based on apparent underreporting by the studied direct primary care provider, of a single class of data: the quantum of primary patient care actually delivered.
Although this data was of central importance and would have warranted a validation process for that reason alone, Milliman evidently took no steps to validate it. But there were clear warning signs warranting extra attention, including the employer’s public reports — known to Milliman — that DPC patients were visiting the DPC clinic three times a year.
Correcting the $8 PMPM to something reasonable shows that Milliman has vastly overstated net savings associated with DPC.
Note: Update of 6/24/2020.
The resources used by direct primary go beyond what has been recorded in CPT codes. DPC docs and advocates used to be the first to tell us that. Here’s a DPC industry leader, Erika Bliss, MD, telling us “how DPC helps”.
A large amount of DPC’s success comes from slowing down the pace of work so physicians can get to know our patients. While it might sound simplistic, having enough time to know a patient is fundamental to providing proper medical attention. Every experienced DPC physician understands that walking into the exam room relaxed, looking the patient in the eye, and asking personal questions dramatically improves treatment. [Emphasis supplied.]
The Milliman report found no net cost savings to Union County from the money it spent on its DPC plan, a negative ROI. But some DPC advocates seek salvation in Milliman’s claim that application of its novel, CPT-code based, isolation model to Union County’s claims data turns that lemon into lemonade.
[T]he DPC option was associated with a statistically significant reduction in overall demand for health care services(−12.64%).
Milliman report at page 7.
As noted, that computation marks overall demand reduction across the system, in which lowered downstream care demands are measured as part of all demanded health care services including the health care services demanded by direct primary care itself.
Lemonade by Milliman — initial steps.
Downstream care utilization for both DPC and PPS patients, along with primary care for utilization non-DPC patients was assumed to be represented by the County’s paid claims. Milliman, in other words, felt it was actuarially sound to use the employer’s negotiated fee schedule as the appropriate yardstick to measure health care services utilization.
But DPC providers are not paid on a claims basis; they are paid on a subscription basis for nearly unlimited 24/7 access, same day appointments, long, slowed down visits, extensive care coordination and the like. How then is the “utilization” of direct primary care services to determined? Is there anything comparable to Union County’s negotiated fee schedule for fee for service medical services that might fit the bill for subscription primary care ?
How about Union County’s negotiated fee schedule for subscription direct primary care from the DPC? An average of $95 PMPM. Had that number been used in Milliman’s alternative model, I note, direct primary care delivered by the DPC would have been “associated with” a substantial increase in overall demand for health care services. Milliman, having found that Union County’s ability to negotiate fees was sauce for the FFS goose, did not find that Union County’s negotiating skill was an appropriate condiment for the subscription DPC gander.
How about setting the utilization of direct primary services at an approximation of market price for subscriptions to bundled primary care services, using perhaps the reports of DPC fees gathered in a survey that was part of the Milliman report? An average of $61 PMPM. Had that number been used in Milliman’s alternative model, I note, direct primary care delivered by the DPC would still have been “associated with” a modest increase in overall demand for health care services. But, hey, what do markets know? Milliman went a different route.
A cost approach, perhaps? I expect that Paladina, Union County’s client, would have declined, if asked, to provide data on the prices it paid for the inputs needed to provide Union County with the contracted direct primary care services. And it could well be that Paladina is as bad a price negotiator as Union County itself.
But these costs can be estimated, and the result would have more general applicability. A very conservative estimate of those costs would be $39 PMPM (based on Union County’s panel size of less than 500, a low PCP compensation package of $175k/yr, and overhead at a low 33% of PCP compensation). Had that number been used in Milliman’s alternative model, I note, direct primary care delivered by the DPC would have been “associated with” a modest decrease in overall demand for health care services of about 5% percent. Using AAFP reports of average PCP salaries and overhead instead of conservative assumptions would turn that number negative.
Using an estimate of the actual costs of putting a PCP into a DPC practice as a means of putting a value on the health care services demanded when a PCP is actually put into a DPC practice seems sensible.
But Milliman took a different course.
Breakthrough in Lemonading: the elements of the Milliman method for computing the health services utilization of direct primary care.
Assume that utilization of subscription-based holistic, integrative direct primary care can be accurately modeled using the same billing and coding technology used in fee for service medicine.
Ignore that a very frequently-given, explicit justification for subscription-based direct primary case is that the fee for service billing and coding methodology can not accurately model holistic, integrative direct primary care.
Ignore that direct primary care physicians as a group loudly disparage coding as a waste of their valuable time, strongly resist it, and do not use standard industry EHRs that are designed for purposes of payment, relying instead on software streamlined for patient care only.
Rely on disbelieving, reluctant DPC physicians, using EHRs ill-equipped for the task, to have accurately coded all services delivered to patients, used those codes to prepare “ghost claims” resembling those used for payment adjudication, and submitted the ghost claims to the employer’s TPA, not to prompt payment, but solely for reporting purposes.
Have the TPA apply the FFS fee schedule to the ghost claims.
Carefully verify the accuracy of the FFS fee schedule amounts applied to the ghost claims.
Do precisely nothing to verify the accuracy of the ghost claims to which the verified FFS fee schedule amounts were applied.
Perform no reality check on the resulting estimate of health care services utilization
Do not compare the results to those articles on Union County you have consulted, referred to and even quoted in your own study.
Do not compare the results to the market prices for direct primary care services revealed in your own study’s market survey.
Anyone see a potential weakness in this methodology?
This methodology resulted in $8 PMPM. That, I note, was the number which when used in Milliman’s alternative model, showed that direct primary care delivered by the DPC was “associated with” a decrease in overall demand for health care services of a 12.6%.
Milliman identifies its methodology as a tidy “apples-to-apples” comparison of FFS primary care services and direct primary care services measured by a common yardstick. But that look comes with the feeling that Milliman have emulated Procrustes, gaining a tidy fit to the iron bed of the fee schedule by cutting off the theoretical underpinnings of direct primary care model.
DPC practitioners, however, are very much bottom-line people who will endure repudiation of their ideology in Milliman’s study details as long as the ostensible headlines serve up something they might be able to monetize: a supposedly “actuarially sound” demonstration that the direct primary care model saves big bucks.
That demonstration, however, hinges on the $8 PMPM result being somewhere near accurate. But that result is at war with reality.
Milliman’s $8 PMPM result defies known facts and common sense — and does indeed contradict the core values of the DPC model.
Whether for the average patient panel size (~450) reported in Milliman’s survey of DPC practices, or for the specific panel size (~500) for the DPC practice in Milliman’s casestudy, $8 PMPM ($96 PMPY) works out to less than $50,000 per PCP per year.That’s not credible.
That Union County DPC patients see their PCP around three times a year is apparent from the public statements of the employer’s then-director of human resources and his successor and even from an article on Union County from which the Milliman study’s literature review quoted verbatim. The three visits are said to have lasted at least half an hour, as long as a full hour, and to be available on same day basis. $96 a year does not pay for that.
Consider also the logical implications of accepting that $8 PMPM yield by Milliman’s process accurately reflected actual office visit duration and frequency for the DPC population. That’s roughly one garden-variety visit per year. In that case, what exactly is there to account for downstream care cost reduction?
Were those reductions in ER visits caused simply by writing “Direct Primary Care” on the clinic door? Were hospital admissions reduced for patients anointed with DPC pixie dust?
What Milliman misses is magic, just not that kind.
It’s the magic of hard, but slowed down, work by DPC practioners. It’s their time doing things for which CPT codes may not or, at least, may not yet exist.* It’s relaxed schedules that assure availability for same day appointments. It’s 24/7 commitments. It’s knowing your patient well enough to ask the personal questions that Dr Bliss mentioned. Collectively this demands more health service resources than are captured by the CPT codes for little more than a single routine PCP visit.
The data set from which Milliman calculated utilization of direct primary care services underreported the patient care given at the clinic.
The only visible path to Milliman’s $8 PMPM figure for health services demand for the delivery of direct primary care is that the direct primary care physicians ghost claims were consistently underreported. That’s a kind of outcome that can reasonably be anticipated when disbelieving, reluctant DPC physicians, using EHRs ill-equipped for the task, are expected to accurately code all services delivered to patients, use those codes to prepare “ghost claims” resembling those used for payment adjudication, and submit those ghost claims to the employer’s TPA, not to prompt payment, but solely for reporting purposes.
In fact, Milliman even knew that the coding habits of the DPC practitioners were inconsistent, in that the ghost claims sometimes contained diagnosis codes and sometimes did not. Report at page 56.
Yet, Milliman did nothing to validate the “ghost claims”.
Whatever the justification for Milliman’s reconstructing the utilization of direct primary care health services demand from CPT codes collected in the situation these were, no meaningful conclusions can be drawn if the raw data used in the reconstruction is incomplete. Milliman does not appear to have investigated whether this key data set was accurate.
As a result of its apparent failure to capture the true resource costs of DPC-covered services rendered by the DPC, Milliman’s determination that the DPC model reduces overall utilization by 12.6% is far too high.
A plausible estimate of the demand for health care services for direct primary care services could be derived from widely-acccepted estimates of primary care physician compensation and practice overhead. Substituting any estimate of those costs greater than $45 PMPM for the $8 PMPM at which Milliman arrived would bring the calculated OVERALL medical services utilization gap between DPC and FFS well below four percent.
Another plausible estimate of the demand for health care services for direct primary care services is the marketprice of DPC services. Milliman’s estimate of that number was $61 PMPM. Substituting that market price for the $8 PMPM at which Milliman arrived turns the health care services utilization gap between DPC and FFS in favor of FFS.
* After the period Milliman studied, for example, CMS came up with 99491.
The lead actuary on Milliman’s study of direct primary care has suggested that the employer (Union County, NC, thinly disguised) would have had a positive ROI on its DPC plan if it had not waived the deductible for DPC members. It ain’t so.
Here’s the Milliman figure presumed to support that point.
It is true that removing the $31 figure of Line H, would lead to a tabulated result of total plan cost of $347, which would suggest net savings.
The problem is that the $61 figure of Line J of the Milliman report has been too low all along — and by more than $31.
Milliman got the $61 by estimating the plan cost of DPC membership, rather than learning what the actual plan cost was. $61 was the result of Milliman applying a 60:40 adult child split to fee levels drawn from Milliman’s survey of $75 adult and $40 child. But the publicly recorded contract between the DPC provider, Paladina, and Union County set the fees at $125 adult and $50 child, and $95 is the correct composite that should have been in Line J, representing $34 PMPM missed by Milliman.
Accordingly, even if the $31 cost that fell on the County for waiving the deductible is expunged from the calculation, the total plan costs for DPC would work out to $381 and would still exceed the total plan costs for FFS. The County’s ROI was indeed negative.
I can not tell you why Milliman used estimated fees of $61 rather than actual fees of $95. But doing so certainly made direct primary care look like a better deal than it is.
The Milliman report’s insistence on the important of risk adjustment will no doubt see the DPC movement pouring a lot of their old wine into new bottles, and perhaps even the creation of new wine. In the meantime, the old gang has been demanding attention to some of the old wine still in the old bottle, specifically, the alleged 68% cost care reductions attributed to Strada Healthcare in its work with a plumbing company of just over 100 persons in Nebraska.
KPI Ninja’s study of Strada’s direct primary care option with Burton Plumbing illustrates why so much of the old DPC wine turns to vinegar in the sunlight.
At an extreme, there will be those who anticipate hitting the plan’s mOOP in the coming year — perhaps because of a planned surgery or a long-standing record of having “mOOPed” year-in and year-out due to an expensive chronic condition; these employees will be indifferent to whether they reach the mOOP by deductible or other cost-sharing; for them, moreover, the $32 PMPM in fixed costs needed for DPC option is pure disincentive. Furthermore, any sicker cohort is more likely to have ongoing relationships with non-Strada PCPs with whom they wish to stay.
An average non-Strada patient is apparently having claims costs of $8000. With a $2000 deductible and say 20% coinsurance applied to the rest that’s an OOP of $3200 and a total employee cost of about $6100; with a $3000 deductible that’s an OOP of $4000 and a total cost of $7250 . Those who expect claims experience of $8000 are unlikely to have picked the DPC/$3K plan. Why $1100 pay more and have fewer PCPs from which to choose?
But what about an employee who anticipated claims only a quarter that size, $2000. With the $2000 deductible that would come to an OOP of $2000 and a total cost of $4860. With the $3000 deductible that would come to an OOP of $2000 and a total cost of $5250. For these healthier employees, the difference between plans is now less than a $400 difference. Why not pay $400 more if, for some reason, you hit it off with the D-PCP when Strada made its enrollment pitch?
The sicker a Burton employee was, the harder this paired-plan structure worked to push her away. It’s a fine cherry-picking machine.
Strada’s analyst, KPI Ninja, recently acknowledged Milliman’s May 2020 report as a breakthrough in the application of risk adjustment to DPC. In doing that, KPI Ninja tacitly confessed their own failure to work out how to reflect risk in assessing DPC for its string of older reports.
To date, as far as I can tell, not one of KPI Ninja’s published case studies has used risk-adjusted data. If risk adjustment was something that Milliman invented barely yesterday, it might be understandable how KPI Ninja’s “data-analytics” team had never used it. But CMS has been doing risk adjustment since the year 2000. It’s significantly older than Direct Primary Care.
KPI Ninja should take this opportunity to revisit its Strada-Burton study, and apply risk adjustment to the results. Same for its Palmetto study and for its recently publicized, but risk-adjustment-free study, for DirectAccessMD. Or this one about Nextera.
Notice that, precisely because they have a higher deductible plan than their FFS counterparts, the Strada-Burton DPC patients faced greater cost-sharing discipline when seeking downstream care. How much of the savings claim in the Strada report owes to the direct primary care model, and how much to the a plan design that forced greater shopping incentives of DPC members?
It’s devilishly clever to start by picking the low-risk cherries and then use the leveraged benefit structure to make the picked cherries generate downstream cost savings.
The conjoined delivery of Strada DPC and enhanced HDHP makes the enhanced HDHP a “confounder” which, unless resolved, makes it virtually certain that even a risk adjusted estimate of DPC effectiveness will still be overly favorable to Strada DPC itself on utilization.
I have no doubt that risk adjustment and resolution of the confounding variable will shred Strada’s cost reduction claims. But, of course, if Strada is confident that it saved Burton money, they can bring KPI Ninja back for re-examination. It should be fun watching KPI Ninja learn on the job.
I’m not sure it would be fair for KPI Ninja to ask Strada to pay for this work, however. KPI Ninja’s website makes plain that its basic offering is data analytics that make DPC clinics look good. Strada may not like the result of a data analytic approach that replaces its current, attractive “data-patina” with mere accuracy.
I’ll skip explaining why the tiny sample size of the Strada-Burton study makes it of doubtful validity. Strada will see to that itself, with vigor, the moment it hears an employer request an actuarially sound version of its Burton study.
Special bonus segment. Burton had a bit over 100 employees in the study year, and a large fraction were not even in the DPC. I’m stumped that Burton had a one-year hospital admission rate of 2.09 per thousand. If Strada/Burton had a single hospital admission in the study year, Strada/Burton would had to have had 478 covered lives to reach a rate as low 2.09. See this spreadsheet. If even one of 200 covered lives had been admitted to the hospital, the inpatient hospitalization rate would have been 5.00.
The use of the 2.09 figure suggests that the hospital admission rate appearing in the whitepaper was simply reported by Strada to the KPI Ninja analyst. A good guess is that it was a hospitilization rate Strada determined for all of its patients. Often, DPC practices have a large number of uninsured patients. And uninsured patients have low hospitilization rates for a fairly obvious reason.
There are three main steps to get from a 19.6% savings claim by Qliance to a plausible number: (1) examining the validity of Qliance’s claim that it collected $251 more per employee than the employers were spending for fees for service primary; (2) including the drug costs which Qliance chose to omit from the data set; and (3) borrowing a generic DPC risk adjustment per Milliman, which brings the number down to 6.8%. Still, I probably wouldn’t bet that DPC can reduce net costs.
STEP ONE — Address the credibility of Qliance’s core claim
In early 2015, Qliance issued a press release that included a table of internal data, a package purporting to show that engagement of Qliance as a direct primary care provider for a subgroup of employees the employers resulted having “19.6 percent less than the total claims” when compared to those employees of the same employers who obtained primary care through traditional fee for service primary care practice. In dollar terms, the reported savings was $679 per person per year. No attempt was made to examine the degree to which the apparent savings might be due to differences in medical risk between the two populations. Some ambiguous wording in the text of the release was clarified by the table itself making clear that the 19.6% savings was intended to be net of Qliance’s monthly direct primary fee. And, a footnote to the table also mentioned that the claims costs analyzed included all claims data except for prescription drug claims.
Here’s the table as presented by Qliance.
The press release and table did not mention the amount of Qliance’s monthly direct primary care fees. These fees do appear, however, in contemporaneous publications such as this article about the Qliance clinic at Expedia. Qliance’s fees to employers were age-dependent, ranging from $49 to $89 per month. Assuming those 65 and older have a top bracket all to themselves, and at least roughly linear age-based pricing for the remaining employees, $64 per month ($768 per year) corresponds to a mid-point and a reasonable estimate of Qliance’s average per employee receipts.
Qliance’s table indicates that the employers’ primary care annual outlay for non-Qliance patients is $251 less Qliance’s annual fee. That would mean these employers were paying $547 per year primary care per employee.
Qliance’s table equates $679 and 19.6% of claims costs, excluding prescription drug costs. Dividing $679 by 19.6% yields $3464 as the total claim costs, excluding the cost of prescription drugs, for non-Qliance patients. The Health Care Cost Institute indicates that in 2014 the average annual prescription drug costs in the West Region where Qliance operates were $684 per person. Adding that amount to their $3464 of other claims costs, brings the total annual employer cost of care for non-Qliance member employees to $4148.
For non-Qliance employees, then, the $547 primary care spend corresponds to over 11.3% of total health care spend. This is a remarkable number. The American Association of Family Physicians expresses horror when it tells us that primary spending falls in the 5-8% range; a recent outgoing AAFP president took great pride for his role in two state intiaitives that pulled the primary care spending percentage into 12-13%. Family Medicine for America’s Health, an alliance of the American Academy of Family Physicians and seven other family medicine leadership organizations.
Presumably, we are to believe that, even though the non-Qliance employees were already approaching the pearly gates of primary care heaven with 11.3 % invested, Qliance’s swooped in and brought them a further 19.6% cost reduction.
Committing $251 more dollars to primary care while netting down $ 679 on total would mean that primary care for the Qliance patients reached 22% of total spend, a fifty per cent increase above the 14% seen in European countries thought to be top performers. A level of primary care spending unknown anywhere other than in Qliance clinics!
All this strongly suggests that Qliance’s math or method is just wrong. But Qliance has not disclosed how the calculation was done. Indeed, as already noted, the Qliance news release proudly claiming large saving Qliance did not even disclosed the monthly fees being paid by employers, an amount that is central to that calculation.
The Qliance table also presents some puzzling details about downstream care. Qliance patients are noted as having 14% fewer ER visits than their FFS counterparts. But the next cell in the same table reports that the average cost of ER claims for Qliance patients was higher, by $5 per annum, than the average cost of ER claims for non-Qliance patients. In percentage terms, an average Qlaince patient incurred ER costs that were slightly more than 14% higher than those of non-Qliance patients.
It is certainly plausible that Qliance patients visiting ERs might present a somewhat different case mix than their counterparts. But Qliance patients having greater average ER costs than their fee for service counterparts stands in sharp contrast to one of the most stressed talking points advanced by advocates for direct primary care.
The Direct Primary Care Coalition is lobbyist for direct primary care. Its current chairman was one of the founders of Qliance. In its advocacy to Congress and others, the Coalition often relies on the 2015 Qliance press release. DPC Coalition has addressed the apparent anomalyregarding ER in an interesting way. In a letter to members of the Senate Committee on Finance, the DPC Coalition produced a modified version of the 2015 table that solved the apparent problem — by simply deleting the data on ER visits!
In the wake of Qliance’s nondisclosure of details and subsequent closure of operations through bankruptcy, I have done a computation based on assumptions which I believe would have been made in the course of due diligence by a potential investor from whom Qlaince sought capital. The assumptions are:
that Qliance received, on average, the mid-point fee of $64 per member per month;
that $684 in prescription drug costs, being an average annual expenditure in the US West Region, should be included in total health care spend;
that non-Qliance patients incurred primary care claims at a rate of 6.7% of total health care spend, which corresponds to the mid-point of AAFP estimates.
Computed in this way, the non-risk adjusted percentage net savings for Qliance patients is 10.7%.
Here is a link to the computation in a downloadable spreadsheet showing all cell formulas. That spreadsheet is imaged here:
If there’s a better way to make this computation, or if I’ve totally blown it, let me know in the comments section.
The reader is also invited to visit the spreadsheet, get it onto their own device, and run their own variations. There is a table that notes how the “$251 assumption” and “19.6% assumption” combine to fix the relationship between putative Qliance fees and corresponding primary care spend percentages for non-Qliance employees. If, for example, you were to assume that average Qliance fee was $49 (despite the report that $49 was the minimum fee), the implicit non-Qliance primary care spend percentage would be 8.1% (a number actually over the top of the range that AAFP reported as typical of the US) and the implicit Qliance primary care spend would be 16.9% (and still well beyond AAFP’s wildest dreams) . In that scenario, the final figure, after risk adjustment and accounting for prescription meds, would still be less than half of Qliance’s initial claim of 19.6%
STEP TWO — Adjust computation to include prescription drug costs
The table above fills the gap created by Qliance’s excludion of prescription drug claims. I do not know the reason for this exclusion. We do know that inclusion of prescription drugs claims in “total claims” would have lowered the amount and percent amount and/or percentage of savings that Qliance reported.
Iora, a direct care practice leader whose work has been featured right alongside Qliance in the legislative advocacy of the Direct Primary Care Coalition, has reported data that certainly make it seem that substantial reductions in other downstream spending can, in effect, be purchased by large increases in prescription usage. In one of its clinics, a 40% increase prescription refills seem to have largely countered huge drops in hospital costs, including ER visits, so that true total spending reduction remained modest.
If Qliance was like Iora in this regard, the inclusion of prescription drug expenses would have significantly reduced what was literally the bottom line of it press-release table.
We don’t know whether Qliance was like Iora.
There appears to be only one careful study explicitly addressing the usage of prescription drugs by DPC patients relative to FFS patients. It’s not Iora’s.
Milliman’s case study for the Society of Actuaries carefully compiled DPC and FFS patient utilization data in all areas of medical care services for an employer contract similar to Qliance’s contracts. For prescription drugs, they measured a 1% greater utilization by the DPC patients.
Applying the Milliman study number to the Qliance work decreased the estimated total annual savings from using DPC by $7. But the largest inpact comes not from deducting $7 from the net savings. Figuring in drug costs increases the denominator of the % savings calculation by $648, so that overall cost savings fall to 16.2% even without any adjustment other than including drug costs.
STEP THREE — Risk adjustment
We urge readers to use caution when reviewing analyses of DPC outcomes that do not explicitly account for differences in population demographics and health status.
The Milliman study for the Society of Actuaries stands alone (in June of 2020) as the only examination by independent experts of the effect on health care costs of demographic and health risk differences between employees who elect direct primary and those who elect traditional primary care. For Milliman’s employer, raw costs needed to adjusted downward by 36% to account for health factors favorable to the employee group that elected direct primary care group.
Should we assume a general similarity between the employees studied by Milliman and the employees of the employers served by Qliance to reflect the health risk characteristics of the sub-population that elected direct primary care? The Milliman study authors note that when an employer offers a direct primary care option — with its exclusive PCP relationship — employees with lower health care needs and fewer anticipated PCP encounters may, ceteris paribus, be more likely to elect DPC. Milliman connects this with the fact that, historically, narrow access plans like HMOs see favorable selection effects relative to PPOs.
The heroic equating of the employee groups from the Qliance and Milliman studies is probably the best available way to address risk issues in the Qliance data. Qliance has been out of business since 2017; it left the field without giving us risk adjusted data. Milliman is, at least for now, is the best we have to try to fill that gap.
Applying the 36% adjustment from Milliman results in a plausible estimate that Qliance adoption was associated with a total cost savings (inclusive of prescription drugs) of 6.8% all employer costs.*
An alternative way to fill the gap draws upon work that is both less sophisticated and less impartial in its analysis, Nextera/DigitalGlobe white paper addressed in this prior post. This was also a much smaller study than Milliman’s and covered a far shorter period of time. It points to a level of risk adjustment within striking range of that from Milliman. Nextera obtained employee claims data for a five month period prior to the availability of a DPC option. The DPC cohort had pre-option claims costs that were lower than the FFS cohort more than 30%. Applying an alternative Nextera-based risk adjustment to Qliance data would have resulted in an estimate that Qliance adoption was associated with a 7.4% overall cost reduction. Due to its provider-independent sourcing, its development by professional actuaries, its larger size and longer duration, I choose to rely on the Milliman adjustment for my headline.
Addendum: July 12, 2020. Benefit plan structure can have a substantial impact on costs and utilization. Although, for the foregoing analysis, I assumed that the employers involved offered employees in the two Qliance and non-Qliance cohorts effectively equivalent cost-sharing obligations, an additional layer of selection bias may well be present if these employers offered significantly different benefit structures to the different cohorts.
In all, I advise against staking anything of value on any claim that Qliance produced net cost reductions. That issue was, in effect, crowd-sourced in early 2017 when Qliance desperately searched for fresh capital before declaring bankruptcy in late Spring.
*This would imply a downstream cost care reduction of about 14%.
Larry A Green Center / Primary Care Collaborative’s Covid-19 primary care survey, May 8-11, 2020:
In less than two months, clinicians have transformed primary care, the largest health care platform in the nation, with 85% now making significant use of virtual health through video-based and telephone-based care.
These words spelled the end of the meme that direct primary care was uniquely able to telmed. “DPC-Telly”, as the meme was known to her close friends, was briefly survived by her near constant companion, “Covid-19 means FFS is failing financially, but DPC is fine”. Further details here.
The study indiscriminately mixed subscription patients with pay-per-visit patients. Selection bias was self-evident; the study period was brief; and the study cohort tiny. Still, the study suggests that choosing Nextera and its doctors was associated with lower costs; but the study’s core defect prevent the drawing of conclusions about subscription primary care.
ADDENDUM of January 2021:In effect, for the seven month duration of the study, the average enrollee in the Nextera option faced a deductible more than $600 higher those who declined Nextera, further skewing results in Nextera’s favor.
The Nextera/DigitalGlobe “whitepaper” on Nextera Healthcare’s “direct primary care” arrangement for 205 members of a Colorado employer’s health plan is such a landmark that, in his most recent book , an acknowledged thought leader of the DPC community footnotes it twice on the same page, in two consecutive sentences, once as the work of a large DPC provider and a second time, for contrast, as the work of a small DPC provider.
The defining characteristic of direct primary care is that it entails a fixed periodic fee for primary care services, as opposed to fee for service or per visit charges. DPC practitioners, their leadership organizations, and their lobbyists have made a broad, aggressive effort to have that definition inscribed into law at the federal level and in every state .
So why then does the Nextera whitepaper rely on the downstream claims costs of a group of 205 Nextera members, many of whom Nextera allowed to pay a flat per visit rather than having compensation only through than a fixed monthly subscription fee?
This “concession” by Nextera preserved HSA tax advantages for those members. This worked tax-wise because creating a significant marginal cost for each visit in this way actually brings this form of non-subscription practice within the intended medical economic goals for which HDHP/HSA plans were created— in precisely the way that a subscription plan, which puts a zero marginal cost on each visit, cannot.
The core idea is that having more immediate “skin the game” prompts patients to become better shoppers for health care services, and lowers patient costs. Those who pay subscription fees and those who pay per visit fees obviously face very different incentive structures at the primary care level. It would certainly have been interesting to see whether Nextera members who paid under the two different models differed in their primary care utilization.
More importantly, however, precisely because the fee per visit cohort all had HDHP/HSAs, they had enhanced incentives to control their consumption of downstream costs compared to those placed in the subscription plan, who did not have HDHP/HSA accounts. The per-visit cohort can, therefore, reasonably be assumed to have been responsible for greater downstream cost reduction per member than their subscription counterparts.
Had the whitepaper broken the plan participants into three groups — non-Nextera, Nextera-subscriber, Nextera per-visit — there is good reason to believe that the subscription model would have come out a loser.
Instead, Nextera analyzed only two groups, with all Nextera members bunched together. And, precisely because the group mixed significant numbers of both fixed fee members and fee for service members, it is logically impossible to say from the given data whether the subscription-based Nextera members experienced downstream cost reduction that were greater than, the same as, or less than the per-visit-based Nextera members. So, while the study does suggest that Nextera clinics are associate with downstream care savings, it could not demonstrate that even a penny of the observed benefit was associated with the subscription direct primary care model.
Here are the core data from the Nextera report.
205 members joined Nextera; they had prior claim costs PMPM of $283.11; the others had prior claim costs PMPM of $408.31. This a huge selection effect. The group that selected Nextera had pre-Nextera claims that were over 30% lower than those declining Nextera.
Rather than award itself credit for that evident selection bias, Nextera more reasoanbly relied on a form of “difference in differences” ( DiD) analysis. They credited themselves, instead, for Nextera patients having claims costs decline during seven months of Nextera enrollment by a larger percentage basis (25.4%) than claim cost for their non-Nextera peers (5.0%), which works out to a difference in differences (DiD) of 20.4%.
Again, the data from mixed subscription and per-visit member can only show the beneficial effect of choosing Nextera, rather than declining Nextera. The observed difference appears to beanice feather in Nextera’s cap; but the data presented is necessarily silent on whether that feather can be associated with a subscription model of care.
It cannot be presumed that Nextera’s success could have been replicated on other DigitalGlobe members.
In the time since the report, Nextera has actively claimed that its DigitalGlobe experience demonstrates that it can reduce claim costs by 25%. Nextera should certainly amend that number to the reflect the smaller difference in differences that its report actually shows (20%). But even that substituted claim of 20% cost reduction would require significant qualification before extension to other populations.
Even before they were Nextera members, those who eventually enrolled seem to have had remarkably low claims costs. Difference in differences analysis relies on a “parallel trend assumption“. The Nextera population may be so much different from those who declined Nextera that the trend observed for the Nextera cohort population can not be assumed even for the non-Nextera cohort from DigitalGlobe, let alone for a large, unselected population like the entire insured population of Georgia.
Consider, for example, an important pair of clues from the Nextera report itself: first, Nextera noted that signups were lower than expected, in part because of many employees showed “hesitancy to move away from an existing physicians they were actively engaged with”; second, “[a] surprising number of participants did not have a primary care doctor at the time the DPC program was introduced”.
As further noted in the report, the latter group “began to receive the health-related care and attention they had avoided up until then.”
A glance at Medicare, reminds us that routine screening at the primary care level is uniquely cost-effective for beneficiaries who may previously avoided costly health care. Medicare’s failure to cover regular routine physical examinations is notorious. But there is one reasonably complete physical examination that Medicare does cover: the “Welcome to Medicare” exam.
First attention to a population of “primary care naives” is likely a way to pick the lowest hanging fruit available to primary care. Far more can be harvested from a population enriched with people receiving attention for a first time than from a group enriched with those previously engaged with a PCP.
Accordingly, a “parallel trend” can not be assumed; and the 20% difference in differences savings in the Nextera group can not be directly extended to the non-Nextera group.
Relatedly, the comparative pre-Nextera claim cost figure may reflect that the Nextera population had a disproportionately high percentage of children, of whom a large number will be “primary care naive” and similarly present a one-time only opportunity for significant returns to initial preventative measures. But a disproportionately high number of children in the Nextera group means a diminished number of children in the remainder — and two groups that could not be presumed to respond identically to Nextera’s particular brand of medicine.
A similar factor might have arisen from the unusual way in which Nextera recruited its enrollees. A group of DigitalGlobe employees with a prior relationship with some Nextera physicians first brought Nextera to DigitalGlobe’s attention and then apparently became part of the enrollee recruiting team. Because of their personalized relationship with particular co-workers and their families, the co-employee recruiters would have been able to identify good matches between the needs of specific potential enrollees and the capabilities of specific Nextera physicians. But this patient panel engineering would result in a population of non-Nextera enrollees that was inherently less amenable to “Nexterity”. Again, it simply cannot can be assumed that the improvement seen with the one group can simply be assumed for any other.
Perhaps most importantly, let us revisit the Nextera reports own suggestion the difference in populations may have reflected “hesitancy to move away from an existing physicians they were actively engaged with”. High claims seem somewhat likely to match active engagement rooted in friendship resulting from frequent proximity. But consider, then, that the frequent promixity itself is likely to be the result of “sticky” chronic diseases that have bound doctor and patient through years of careful management. It seems likely that the same people who stick with their doctors are more likely to have a significantly different and less tractible set of medical conditions than those who have jumped to DPC.
Absent probing data on whether types of different health conditions prevail in the Nextera and non-Nextera populations, it is difficult to draw any firm conclusion about what Nextrea might have been able to accomplish with the non-Nextera population.
These kinds of possibilities should be accounted for in any attempt to use the Nextera results to predict downstream cost reductions outcomes for a general population.
Perhaps, the low pre-Nextera claims costs of the group that later elected Nextera reflects nothing more than the Nextera group having a high proportion of price-savvy HDHP/HSA members. If that is the case, Nextera can fairly take credit for making the savvy even savvier. But it cannot be presumed that Nextera could do as working with a less savvy group or with those who do not have HDHPs.
Whether or not Nextera inadvertently recruited a study population that made Nextera look good, that study population was tiny.
Another basis for caution before taking Nextera’s 20% claim into any broader context is the limited amount of total experience reflected in the Nextera data — seven months experience for 205 Nextera patients. In fact, Nextera’s own report explains that before turning to Nextera, DigitalGlobe approached several larger direct primary care companies (almost certainly including Qliance and Paladina Health); these larger companies declined to participate in the proposed study, perhaps because it was too short and too small. The recent Milliman report was based ten fold greater claims experience – and even then it had too few hospitalizations for statistical significance.
Total claims for the short period of the Nextera experiment were barely over $300,000, the 20% difference in difference for claimed savings comes to about $60,000. That’s a pittance.
Consider that two or three members may have elected to eschew Nextera in May 2015 because, no matter how many primary care visits they might have been anticipating in the coming months, they knew they would hit their yearly out-of-pocket maximum and, therefore, not be any further out of pocket. Maybe one was planning a June maternity stay; another, a June scheduled knee replacement. A third, perhaps, was in hospital because of an automobile accident at the time for election. Did Nextera-abstention of these kinds of cases contribute importantly to pre-Nextera claims cost differentials?
The matter is raised here primarily to suggest the fragility of a purported post-Nextera savings of a mere $60,000 over seven months. An eighth month auto accident, hip replacement, or Cesarean birth could evaporate a huge share of such savings in a single day. The Nextera experience is too small to be reliable.
Nextera has yet to augment the study numbers or duration.
Nextera has not chosen to publish any comparably detailed study of downstream claims reduction experience more recent than 2015 data — whether for DigitalGlobe or or any other group of Nextera patients. That’s a long time.
Nextera now has over one-hundred doctors, a presence in eight different states, and patient numbers in the tens of thousands. Shouldn’t there be newer, more complete, and more revealing data?
Because of its short duration and limited number of participants, because it has not been carried forward in time, because of the sharp and unexplained pre-Nextera claims rate differences between the Nextera group and the non-Nextera group, and because its reported cost reduction do not distinguish between subscription members and per-visit members, the Nextera study cannot be relied on as giving a reasonable account of the overall effectiveness of subscription direct primary care in reducing overall care costs.
January 2021 Addendum:An additional study design defect skews results in Nextera’s favor.
June 1 is an odd time to start a health expenditure study, coming as it does near the mid-point of an annual deductible cycle. In the five months prior to the opportunity to enroll in Nextera, those who declined Nextera had combined claims that averaged $2041, while those who opted for Nextera had combined claims of only $1420. The average employer plan in the US in 2015 had a deductible of $1318 for single employee. Whatever the level at Digital Globe, it is quite certain that the group that eschewed Nextera had significantly more members who had already met their 2015 deductible than those in the Nextera group, and more who were near to doing so.
Note that Indeed, any employees who had already met her annual deductible at the time Nextera became available would have had deductible-free primary care for the rest of the year, whether she joined Nextera or not. She would have gained nothing by choosing Nextera, but may well have had to change her PCP to one of the few on Nextera’s ultra-narrow panel.
More importantly, however, it is well known that, in the aggregate, once patients have cleared their deductible for an annual insurance cycle, they increase their utilization for the rest of the cycle. On the other hand, patients who do not envision meeting their deductible tend to deer utilization. Ask any experienced claims manager what happens in November and December.
Higher relative claims going forward for the non-Nextera group would be entirely predictable even if the Nextera and non-Nextera populations had had precisely equal risk profiles.
Nextera’s case study also had errors of arithmetic, like this one:
The reduction rounds off to 5.0%, the number I used in the larger table above.
A subscription model is not the most patient-centered way.
Consider this primary health care arrangement:
Provider operates a cash practice
no insurance taken
no third party billed
Provider may secure payment with a retainer
balance is carried
refreshed when balance falls below a set threshold
Provider may bill patient for services rendered on any basis other than subscription
specific fees for specific services; or
flat per visit fee for all patients; or
patient-specific flat visit fee, based on patient’s risk score; or
patient-specific flat visit fee, based on affinity discounts for Bulldog fans; or
fee tiers based on time/day of service peak/off-peak; or
fee tiers based on communication device: face-to-face/ phone/ video/ drum/ smoke signal; or
any transparent fee system based on transparent factors; but
Provider elects not to bill a subscription fee, e.g., she does not require regular periodic fees paid in consideration for an undetermined quantum of professional services.
The plan above is price transparent to both parties. It is more transparent than a subscription plan because it is easier for each party to determine a precise value of what is being exchanged.
The plan above gives a patient “skin in the game” whenever she makes a decision about utilization.
Patient and doctor have complete freedom to pair and unpair as they wish. There will no inertial force from the presence of a subscription plan to interfer with the doctor-patient relationship.
The patient gets to use HSA funds, today. The plan above is fullly consistent with existing law and its policy rationale; a subscription plan is not.
Precisely because this plan beats subscription plans on freedom, transparency, and “skin in the game”, this plan is likely to lower your patient’s total costs better than a subscription plan — even if your patient does not have an HSA.
The specific fees and fee-setting methods will be disciplined by market forces. Some providers, for example, might find that the increased administrative costs of a risk-adjusted fee are warranted, while other stick with simpler models. Importantly, forgoing subscription fees should reduce the market distortions that arise when contracts that allocate medical cost risk between parties.
Health care economics has lessons about cherry picking, underwriting and death spirals, dangers associated with increased costs. These dangers have palpably afflicted health insurance contracts. Subscription service vendors are not immune. A subscription-based PCP unwilling to pick cherries will be left with a panel of lemons.
HDHP/HSA plans were created as a countermeasure to the phenomenon described by Pauley in 1968 , that when “the cost of the individual’s excess usage is spread over all other purchasers of that insurance, the individual is not prompted to restrain his usage of care“. A state legislature declaring that subscription medicine “is not insurance” does nothing to check the rational economic behavior of a DPC subscriber with no skin to lose when seeking her next office visit.
Some who generally do subscription medicine have, for years, also used per visit fees like those suggested above to address concerns about HSA accounts. In fact, one of the more widely touted self-studies by a direct provider, Nextera’s whitepaper on Digitial Globe, supported its claim of downstream claims cost reduction by comparing traditional FFS patients and a “DPC” population that included a significant proportion of per visit flat rate patients. Although Nextera claims that its study validates “DPC”, it presented no data that would allow determination of which DPC model – subscription or flat rate – was more effective.
In fact, before the end of March 2020, several DPC practices responded to the pandemic by offering one-time flat-rate Covid-19 assessment to non-members, such as non-subscribed children or spouses of subscribed members. Those flat-rated family members would have been able to use HSA funds for that care in situations in which the actual members might well have been unable.
I urge the rest of the no-insurance primary care community to reconsider its insistence on a subscription system that simultaneously reduces the ambit of “skin in the game” and cuts off the access of 23 million potential patients to tax-advantages HSAs. There’s a better way — less entangled with regulation, less expensive, more free, more transparent, and even more “patient-centered”.
UPDATE: IRS showed in recent rulemaking process that it fully believes DPC subscription fees are, by law, a deal breaker for HSAs, despite the president* signalling his favor for DPCs. In my opinion, IRS would prevail in court if it cared to enforce its view. Philip Eskew of DPC Frontier is 100% correct that the odds of the IRS winning on this are closer to 10% than they are to 1%, just not in the way he apparently meant it.
Union County is estimated by Milliman to have lost money. The odds that Union County saved more than 5.2% are less than one in twenty. The odds that Union County saved 28% or anything near that are miniscule.
Do you remember when DPC was claimed to be saving Union County $1.25 Million per year? So why did Union County’s health benefits expenditure rise twice as fast as can be explained by the combined effect of medical price inflation and workforce growth?
On May 13th, the Direct Primary Alliance published a manifesto: Building the Path to Direct Primary Care. It was signed by every officer and board member of the largest membership organization of direct primary care physicians.
In so many words, it said:
FFS primary care practice is being destroyed, financially, by the Covid-19 pandemic.
DPC is thriving, financially.
DPC has always been great, and has always been superior to FFS.
Because of the pandemic, DPC is now even greater and even more superior to FFS.
DPC will be even greater than it is now and even more superior to FFS than it is now, if we get help from government, insurers, employers, patients and everyone else.
DPC achieves lower overall healthcare spending.
DPC Alliance will help FFS practicitioners transfer to DPC.
In a recent post, I addressed the DPC-PATH’s claims regarding how well, relative to FFS practices, DPC practices were weathering covid-induced financial stress.
Here I turn to DPC-PATH as representing DPC Aliance’s clearest statement yet of the perennial claim by DPC advocates that “Direct pay primary care models provide health care purchasers with a means to achieve lower overall healthcare spending.5 6“.
This was a lengthy, exhaustive study, of a large number of employees of a single employer, and it featured serious efforts at adjusting for demographic factors. The employees were offered the option of receiving primary care through traditional community PCPs or through either one on-site or fifteen near-site employer-sponsored clinics. It may well be the soundest study ever to show success in a primary care cost savings initiative. The study found savings of $167 PMPM, 45%, for those using primarily the on-site, near-site clinics delivery model.
But that delivery model was absolutely not direct primary care. Every employee visit in both the “treatment” group and the “control” group was reimbursed to the providers on a fee for service basis by the employer and/or employee cost sharing (a mix of deductibles, co-pays, and coinsurance).
In other words, what DPC Alliance’s manifesto presented as its first piece of evidence that direct primary care can save money was an article that seemingly demonstrated that certain FFS-based primary care delivery clinics saved money.
Interestingly, the Basu article on FFS on-site, near-site clinics in DPC-PATH’s footnote 5 more or less steps on the second bit of evidence purported, by DPC Alliance in Footnote 6, to show that DPCs reduce cost. That footnote links to the claimed savings of 28% for a DPC option in the employee health plan of Union County, NC.
Surprise! DPC is offered in Union County through a proudly touted near-site clinic. So, the article presented by DPC-PATH Footnote 5 suggests that the results shown in the article presented by DPC-PATH Footnote 6 can be explained by the location of the Union County clinic rather than the payment model under which the Union County clinic operates.
More importantly, however, the Union County DPC plan is the best studied plan in the entire direct primary care universe. DPC advocates have bragged about it again and again (1k hits for “Union County” and “direct primary care”).
… [T]he introduction of a DPC option increased total nonadministrative plan costs for the employer by 1.3% after consideration of the DPC membership fee and other plan design changes for members enrolled in the DPC option.
Apparently, not even using a near-site clinic could make DPC a money saving proposition for Union County. In fact, I show in a separate post that the DPC option likely increased Union County’s costs for covered employees, not by a mere 1.3%, but by nearly 8%.
In February 2017, I sent the op-ed piece below to the Charlotte Observer. It was not selected for publication. But it has been proven accurate in a detailed, independent study by a team of health care actuaries from the Milliman firm, known widely for its health care work. The study was prepared for the Society of Actuaries. See discussion below my op-ed.
Union County, scene of an 1865 dust-up involving General Sherman’s troops, is now the site of a skirmish in the national civil war over health care policy. Katherine Restrepo, Director of Health Policy at North Carolina’s John Locke Foundation, has been calling attention across the South, and in Forbes, to the county’s experience with a health care delivery vehicle known as direct primary care, or DPC.
In the Union County employee health system, all enrollees have insurance to cover most types of medical services other than primary care. For the latter, they have a choice between receiving primary care from hundreds of traditional insurance-based physicians, subject to deductibles and copayments, or receiving primary care exclusively from a small closed panel of physicians at a pre-paid insurance-free direct primary care clinic with no deductibles or copayments. According to its supporters, the primary clinic’s savings in insurance overhead allows its providers more time for patient care, which in turn curbs the need for expensive specialists, emergency rooms, hospitals, and costly medications.
When Union County created a direct primary care option for its employees and their dependents in 2015, a bit under half of them elected the DPC. When compared with the traditional plan, according to Ms. Restrepo, the direct care plan saved the county as much as 28% in medical expenses, an impressive $1500 per insured per year.
With claimed savings like that, she and other small-government advocates are eager to bet the health of every state and local government employee on DPC. They seem particularly eager to promote direct primary care as the core model for Medicaid.
But there are problems.
Unless asked directly, DPC advocates withhold the fact that the enrollees in the direct primary care group are five years younger than those in the traditional care group.
Age matters though, and it matters a lot. Age-cost curves for health care are steep. In tirades against the Affordable Care Act, many conservatives insist that the costs for 64-year-olds are five times higher than costs for 21-year-olds; that insurance premiums should reflect this 5:1 ratio; and that the 3:1 curve mandated by the Affordable Care Act penalizes the relatively young.
As an interim step pending ACA repeal, the Trump administration recently floated the idea of moving to an age-premium curve of 3.49:1. On that curve, a five-year gap in age would explain every penny of the difference between the health costs of the two Union County populations.
The 5:1 curve would imply that offering the direct primary care program actually cost Union County well over $600,000.
Furthermore, DPC advocates make no adjustments for prior health experience. For example, patients with multiple health issues of long standing might choose to avoid the direct primary care clinic’s small, closed panel so that they can keep an established relationship with their traditional primary care physician; it makes medical sense.
There are rigorous ways of evaluating whether Union County’s costs savings reflect some innate superiority of direct primary care or merely that the relatively healthy preferred a different plan than their less healthy counterparts. Restrepo compares group costs, but fails to carefully assess whether health status differences between the groups might be driving the “savings”.
Let’s not bet the health care of county enrollees, Medicaid recipients, or anyone else on the idea that little Union County won big savings by offering direct primary care. A far safer bet is that Union County’s decision makers managed only to segment their enrollee population by health status, then proclaim an unjustifiable win for a still-unproven health care concept.
An mistaken presumption in my op-ed
The calculations in the op-ed were based on there being a five year age difference between the two groups, my best estimate at the time. Later in 2017, the County advised me that the difference was almost exactly four years. Accordingly, my estimate of net County loss under a 5:1 curve should have been closer to $400,000.
Milliman’s study conclusion
Here’s the core conclusion from the Milliman firm:
[T]he introduction of a DPC option increased total nonadministrative plan costs for the employer by 1.3% after consideration of the DPC membership fee and other plan design changes for members enrolled in the DPC option.
Milliman’s total cost computation was based on estimates monthly DPC of $75 per adult and $40 per child; using those numbers, the 1.3% increase corresponds to $7 per member per month, a net loss to the County of $6,000 vs a claimed savings of about $1.3 million.
Milliman’s one major error: its estimates of monthly fees were far too low.
Apparently Milliman’s team did not realize that, instead of estimating the month fees, they might have simply looked in the public record. The contract between the County and the provider set monthly fees at $125 per adult and $50 per child. Direct primary care cost Union County, not $7, but $41 per member per month — about $430,000 per year.
The deepest significance of the high DPC fees in Union County is not that the county lost a lot of money. Rather, it is that it took a very large investment to gain the downstream cost reductions, which were largely driven by reduced ED visits. $430,000 a year will easily fund an additional PCP to simply do phone calls and housecalls intended to intercept unnecessary ED visits, effectively attaching a glorified doc-in-the-box to the clinic. In fact, all care in the Union County DPC was provided by Board Certified Family Physicians. Without that extra money, i.e., with a $75/adult budget, it seems doubtful that a DPC clinic could accomplish ED visit reduction at even the modest standard at Union County.
Reality: while it is may not be a pretty picture, no one has a clear view what the pandemic’s ultimate effects on primary care practices, FFS or DPC, will be.
On May 13th, the Direct Primary Alliance published a manifesto: Building the Path to Direct Primary Care. It was signed by every officer and board member of the largest membership organization of direct primary care physicians.
In so many words, it said:
FFS primary care practice is being destroyed, financially, by the Covid-19 pandemic.
DPC is thriving, financially.
DPC has always been great, and has always been superior to FFS.
Because of the pandemic, DPC is now even greater and even more superior to FFS.
DPC will be even greater than it is now and even more superior to FFS than it is now, if we get help from government, insurers, employers, patients and everyone else.
DPC achieves lower overall healthcare spending.
DPC Alliance will help FFS practicitioners transfer to DPC.
In this blog, I’ve dealt previously with several of these issues, but today’s special attention goes to the new information about financial viability in mid-May 2020 that came to my attention through the DPC-PATH manifesto itself.
For its key financial arguments, the manifesto relies on an end of April survey of primary care practices , including some DPC practices, by the Larry A Green Center. That center highlighted that an astonishing 32% of PCP respondents said they were likely to apply, in May, for SBA/PPP Covid-emergency money. That means a lot of PCPs expected to certify either they have suffered a significant economic harm because of the current emergency (SBA-EIDL) or that a loan is “necessary to support on-going operations”.
I don’t think DPC Alliance should be bragging about how much better DPC is weathering a pandemic than FFS with a survey that indicates that DPC docs were 60% more likely to seek emergency assistance this month than their FFS counterparts.
When this survey result was brought to the attention of some DPC Alliance board members, some offered the small size of most DPC practices as an explanation. I was told they feared “doom” and that they applied for government help because of the economic uncertainty coupled to their fear that they would not get government help. Interesting rationale!
But I was also told that it was reckless of me to think that DPC practices who certified to a good faith belief that uncertain economic conditions make their PPP loans necessary actually believed what they certified. Yet, strange as it is for DPC advocates to suggest that some DPC practitioners had committed felonies, one advocate earned “likes” from DPC advocates when he hammered the point home by cheerfully noting that the SBA had announced that PPP loans under $2 million would not be audited.
In fact, the SBA did not announce this non-audit policy until more than two weeks after the Green Center survey. Even then, the policy was carefully explained as intended to relieve smaller businesses from the financial burden of audit (not from the consequences of crime — fines up to $1 million and 30 years imprisonment). When DPC docs say they needed PPP loans to maintain current operations, I believe the docs and not those who accuse them of committing felonies.
On the other hand, there are clear advantages that DPC practices have had over PPS in weathering, financially, the first few months of the pandemic.
Relative to FFS practices, DPCs are concentrated in states with lower infection rates; there is less shutdown, less lost wages, less social distancing, less risk to office visits, less public panic.
Also, DPC practices do not accept Medicare, and have relatively tiny numbers of elderly patients relative to FFS practices. In average FFS- PCP practice during normal times, about one-quarter of patient visitors are over 65. But it is elders who, presently, have the strongest incentives to cancel office visits, to postpone routine care, and even to forgo minor sick visits or urgent care. Even in Georgia, the first state to “reopen”, the elderly remained subject to a gubernatorial stay at home order. FFS is taking a current revenue hit on patients who are barely visible in DPC practices.
That DPC providers tend to be located in less infected states and that their patient panels are nearly devoid of seniors means that DPC practices have likely caught a financial break relative to FFS. In terms of long-term policy goals and health care costs, however, DPC has found nothing in its response to the Covid crisis to brag about.
How will DPC practices compare to FFS practices six months or a year from now?
If Covid-19 survivors have a surge of primary care needs, DPC practices could be obliged to deliver more care for previously fixed revenue, but FFS practices are likely to be more able to match rising patient needs to rising revenues.
If social distancing continues to keep the number of in-office visits depressed, the perceived value of what was sold to patients as high-touch medicine will fall and subscribers may insist on lower subscription fees.
If the economy stays in the tank, patients may pay more attention to whether DPC gives good value. DPC would do well if those 85% cost reduction claims were anywhere near valid. But there is extremely little evidence to support the cost-effectiveness brags of DPC providers. Instead, there is solid actuarial evidence that can DPC increases cost.
Reality: while it may not be a pretty picture, no one has a clear view what the pandemic’s ultimate effects on primary care practices, FFS or DPC, will be.
A 1.8 billion dollar subsidy to support subscription-model contraction of primary care patient panel sizes is a problematic policy in a country when there is a shortage of primary care physicians.
I came to this trying to figure something out. We hear that Ron Wyden kept the DPC/HDHP fix for subscription fees out of the CARES Act. DPC Coalition’ s Jay Keese flatly indicated that this was because Wyden was confused about the relationship between DPC and concierge. Because Wyden is a pretty wonky guy, and his wonkiness extends especially to health care policy, I just don’t believe that his concerns are so simple they can be addressed by explaining that “DPC is not concierge”; I’ll bet he understands the differences as well as anyone.
Differences do not always make the difference. Sometimes the similarities matter more.
It matters not how much DPC and concierge differ on some or even most possible variables, if DPC and concierge are, at the same time, similar on one or more of a set of decisive variables.
Most likely, Wyden’s biggest concern is to avoid using the tax code to support subscription fees that buy, in large part, exclusionary access to PCP services that are in short supply.
700 member patient panels at DPC clinics literally exclude the 701st and all additional patients. If there were plenty of PCPs to go around this fact would be less significant. DPC cannot be sufficiently scaled for everyone, or even most people, to have DPC in any near future. In fact, if every PCP goes to a 700 person panel today, tens of millions who had a PCP yesterday would not have a PCP tomorrow. This is precisely what subscription based small panel DPC shares with concierge practices: more attention for some comes at the price of less attention for others.
Why should taxpayers subsidize that?
One can image basing a possible answer to that question on real data to demonstrate that the cost-or-health effectiveness of DPC creates off-setting value. But, as far as I can tell, and this blog closely follows the barrage of brags by DPC advocates, there is as yet no independent, peer-reviewed study to support the proposition that DPC is cost-effective, not even for its own members. Not one.
Even if what is needed is a larger pool of PCPs, why not directly subsidize primary care practice. A tax fix for subscription fees is a roundabout way of getting that result, and compounds this issue of access inequality with issues of wealth inequality.
If one wishes to determine what the law should do about ________,he can approach the question in either of two ways: by definition or by analysis.
The article by Roger Dworkin explains why it is problematic to try to solve real problems simply by invoking definitions. In this context, that means it is hard to resolve the issues by saying that “DPC is by definition not the same thing as concierge” Here, the reasons which apply to denying public financial support to concierge practices apply in the same general way, if to a lesser degree, to DPC subscription fees. To solve policy problems, decision makers need to look at broad effects, not mere word formulas.
October 20, 2019: 500+ word Open Letter to Members of Congress by DPC Coaltion President asking for support and co-sponsorship of the The Primary Care Enhancement Act. Missing words: telehealth, telemedicine, virtual, telephone, phone, text message, text, SMS.
March 26, 2020: DPC Coalition laments exclusion of the bill from CARES despite being sold as “means of expanding virtual care to 23 million more Americans with HDHP/HSA plans.”
Fortunately, all 23 million HDHP members dodged that bullet when a huge swatch of FFS primary care docs (along with DPC docs willing to code) stepped up to virtual care practically overnight.
In literally a week we have had 50 providers convert to providing a virtual care model that includes phone-visits, e-messaging, and video visits. We’ve seen the mindset shift from considering what we might use telehealth for to what we can’t use telehealth for. In just one week we have transitioned 50 percent of our clinic visits to a virtual format.
It is likely that on a single day or two last week, (3/23 to 3/27) the number of FFS PCPs who learned to telemed exceeded the total number of DPC docs present in country. By April 1, there should be many fold more telemeding FFS docs than telemeding DPC docs. [Indeed, a U.S. Senator from Georgia bet on that a month ago, buying telemedicine-related stock based insider information about the impending disaster. ]
When Brain Forrest MD, the founder of the Access Healthcare direct primary care clinic, does legislative advocacy at, for example, the United States Senate, he shows the data of the foregoing chart. It’s from a 2013 course project by three NC State post-baccalaureate management students. He advocates pro-DPC legislation, apparently telling policy makers that the NCSU students found that, over a ten year period, Forrest’s patients’ total costs of care were lower than even than the lowest of the selected industrialized countries, and had remained flat at $2200 a year through Forrest’s ten years in direct pay practice.
That $2200 figure is composed from an estimate of the annual fees for subscription members of Forrest’s DPC clinic coupled with a catastrophic coverage insurance policy priced at $1750. After passing through the hands of Forrest’s allies in the public policy arena, this soon became a proposal by the Georgia Public Policy Foundation for an alternative to Medicaid expansion, for about 400,000 low-income Georgia adults, that would provide each of them with a catastrophic coverage insurance policy and a direct primary care subscription. The Foundation prices this “patient-centered” option at between $2000 and $3000 per year, a fiscal conservative’s dream when compared to the $5370 per so-called “expansion adult” projected for 2018 by CMS’s chief actuary.
But even $3000 does not come close to providing adequate funding for the health care needs of the Medicaid expansion population. The Foundation’s model, like Forrest’s claim that his DPC patients pay the lowest amounts in the industrialized world, seemingly rests on a massive error. The calculations Forrest presented reflect a patient population that carried high-deductible catastrophic policies but paid not a penny of cost-sharing for any downstream care. It is absurd to suggest that any typical patient panel will have a similar result.
Some DPC advocates seem to believe that there is some sort of “true catastrophic coverage”, under which anything beyond primary care is a “true catastrophe” for which an insurer will pay all or nearly of the total cost. Such policies do not seem to actually exist. If they did exist, the premiums would likely be quite high, comparable to those of platinum policies on ACA exchanges. In any event, a fantasy of this sort provides a foundation for the delusion that “DPC + a cat” can meet the health care needs of indigents.
To get some idea what health care for indigents might actually cost, we can start with looking at catastrophic policies as they exist, today, in Georgia. A 42 year old (average age for expansion adults) Atlanta resident can have catastrophic coverage for $3200 per year; it comes with a deductible of $8150.
It has an actuarial value of less than 60%; so, annual cost-sharing would average at least $2133 for each covered person. Adding the cost-sharing and the premium, annual expenses for a covered person of average age and with average experience would come to $5333.
Even a $3000 version of the Foundation’s program would be insufficient to pay the premium of a catastrophic coverage policy for an indigent adult of average age. And even with a “cat” policy in hand, and primary care prepaid, an average indigent patient would still need massive financial assistance to meet an average patient share of downstream care costs.
If there were sound evidence that direct primary care can actually produce net cost savings, the care of that average expansion adult might be brought below $5333. Since there is no sound evidence that direct primary care can do that, however, Medicaid expansion at $5370 completely reasonable.
Bonus Segment 1. The cost of DPC+Cat were not flat for ten years.
It is quite unlikely that the costs for Forrest’s patients at Access Healthcare, even just those for DPC fees and catastrophic premiums, stayed constant from 2002 through 2013. Medical cost inflation, per the Bureau of Labor Statistics rose about 50% over that period. An insurance policy comparable to one that cost $1750 in 2013 should have cost only $1167 in 2002.
As to the direct primary care fees at Access Healthcare, the students found that the average member in 2013 had 3.7 clinic visits, for which he would have paid $473. Dr. Forrest himself has published rates for his own practice in 2002 that would have priced 3.7 visits at $285. Forrest’s 2013 fees were actually 65% greater than his 2002 fees; he was raising fees even faster than the general rate of medical cost inflation.
Forrest’s patients’ cost curve flexes upward, like those for every country shown.
Bonus Segment 2. The $1750 premiums in the Forrest calculation reflect the exclusion of those with pre-existing conditions.
The relationship between 2013 catastrophic policies to those in 2020 is less straightforward. Above I used a $3200 policy from 2020; had it existed, the same policy if deflated to a 2013 value (using BLS information as in the previous segment) would have cost about $2600, $900 more than the $1750 in Access Healthcare Calculation.
The difference between the policy pricing is that the 2013 figure of $1750 is pre-ACA and would have been underwritten; risky customers were broadly excluded or, if allowed, were subject to exclusions and waiting periods.
Presumably, a program of healthcare for indigents requires significant parity of access for the individuals at all risk levels. One way or another, the costs of risky indigents has to figure in. Realistic “cat” pricing in 2013 would have been $2600 for a community rated policy, or would have averaged $2600 for a series of underwritten policies covering all ages/risks level in separate pools.
Bonus Segment 3. The United States’ series line in the chart above is not representative of either Forrest’s patients or the Medicaid population.
The charted figures for total healthcare cost of various nations shown above include basic medical care for the particularly expensive aged population, as well as the cost of custodial long term care for those, old or young, who receive it. In the US, these items are paid for in systems that are essentially separate from either the target Medicaid expansion population or Forrest’s patient panel.
Bonus Upshot of Bonuses.
Adjust Forrest’s patients’ cost curve upward so it no longer excludes downstream care costs born by real patients;
Further adjust Forrest’s patients’ cost curve upward so that it includes the cost of catastrophic insurance for the full range of real, non-aged patients, including the risky;
Adjust the curve of the United Sates downward so it reflects the non-Medicare population and excludes long-term care expenses;
Give the correct upward curving form to Forrest’s patients’ cost curve; and
Viola — Forrest’s patients’ cost curve will look a hell of a lot like everyone else’s.
Health Programs Group, University of Wisconsin School of Medicine and Public Health, Population Health Institute. Direct Primary Care (DPC): Potential Impact on Cost, Quality, Health Outcomes, and Provider Workforce Capacity, A Review of Existing Experience & Questions for Evaluation, October 8, 2019. On-line publication.
The thing speaks for itself, acknowledging potential and noting absence of proof.
Also makes clear that how much my own analyses misses a hell of a lot.
Not more than a quick look at this, for example, made me realize that old comparisons of OOP for DPC primary vs FFS primary – such as the one mentioned in this previous post – were likely to be shifted significantly in more recent years even further in favor of FFS because of the ACA rule barring application of cost-sharing for a list of designated preventative services. Note, too, that the bar applies to high-deductible plans.
In a May 2018 “Policy Position” for the John Locke Foundation, Kathleen Restrepo wrote the following:
A study conducted by University of North Carolina and North Carolina State University researchers found that patients seeking treatment from Access Healthcare, a direct-care practice located in Apex, North Carolina, spent 85 percent less on total health care spending and enjoyed an average of 35 minutes per visit compared to eight minutes in a nondirect-care practice setting.
Let’s carefully address her sourcing and find out.
Restrepo misrepresented the provenance of the 85% claim.
If you thought that Restrepo’s hyperlink from the word “study” to an article in a peer-reviewed academic journal would take you to an academic report of the study by a team of academic research professionals, you were wrong. Restrepo’s statement is not your ordinary reference to a piece of peer-reviewed academic research.
Restrepo gives a fourth-hand account of unpublished material by medical students and business school students engaged in course work projects. The published article by Eskew and Klink, to which Restropo provided a rather misleading link, gives a third-hand account of the research Restrepo describes; the second-hand account of that particular research comprised less than three minutes and three powerpoint slides in a meeting presentation by Dr. Brian Forrest.
The business school students’ part of the work was never compiled into a manuscript, although the students made slides and presented them in several closed-to-the public venues (personal communication with Charles Queen, one of three business student authors named by Forrest ). Forrest’s talk also included a thirty second summary of separate work by an unstated number of unidentified medical students.
Along with the identity of the originators of any work referred to, the very fact of publication and the details of publication are, of course, important initial indicators of the credibility of cited research. Even high-school students are taught to fully and accurately represent the provenance of the material they reference. Restrepo knew that the relevant work was enitirely by students (see her earlier policy piece), but eschewed revealing that telling detail to those she sought to influence. More importantly, even though the Restrepo-cited Eskew and Klink article plainly stated that the actual research was unpublished, Restrepo disguised that unpublished research by dressing it in the garb of a peer-reviewed published article.
Restrepo did not accurately convey the content of the article she cited; and that article had not accurately conveyed the content of the source it cited.
High-school students are also taught that they must accurately represent, not just the provenance of claims on which they rely, but also the substance of the material to which they refer. Yet it seems that Restrepo’s fourth-hand account may have failed even to accurately convey what was said in Eskew and Klink’s third-hand account. Eskew and Klink (“EK”) say the study showed that DPC patients “spend 85% less out of pocket for their total cost of care compared with the same level and amount of care in a traditional setting.” Restrepo offers instead that DPC patients “spend 85% less on total health care spending”. These seem to mean quite very different things. Dr. Eskew has confirmed to me that he was referring to primary care cost sharing for insured FFS patients. But primary care costs are only a part of “total health care spending”, referred to by Restrepo.
Perhaps Procrustes could fit Eskew, Klink, and Restrepo on the same page. If so, that page should be the Forrest presentation that Eskew and Klink identified as their source. But neither Restrepo’s fourth-hand account nor Eskew and Klink’s third-hand account accurately reflects Forrest’s representation of what the research team itself had to say about comparative savings cost savings for DPC versus traditional patients.
The 33rd minute of his talk was the only point at which Dr. Forrest referred to comparative cost savings of DPC versus traditional patients as determined by NCSU business students. For this, he showed a slide by those students which made exactly one cost comparsion: that of the employee share of premium for various employer sponsored insurance policies versus the full premium of a catastrophic policy ; the students computed a differential of 33%.
The 18th minute of his talk was the only point at which Forrest referred to any specific work by UNC medical students. There was no slide, but he said this, and this alone: “In fact, some work by some UNC medical students showed that people who were commercially insured actually came out of pocket 7% cheaper for the year when they came to our practice versus ten other local practices that were in the traditional model that were in network.” I have repeatedly asked Dr. Forrest for copies of any reports made these students or that he identify them; he has not answered.
Neither a 7% difference in OOP nor a 33% difference in insurance premiums bears much resemblance to the 85% reductions in whatever it was Eskew, Klink, and/or Restrepo (EKR) had written about. No 85% figure was tied to any student research finding anywhere in Forrest’s presentation. Somehow, the entire EKR trio found themselves in contradiction to the very report that announced the existence of the studies to which they referred!
Nothing could better demonstrate why it is broadly agreed that referrers should carefully examine the material to which they refer. This is precisely why the rules of citation prioritize primary reports of research results. Indeed, even when citation of secondary reports is allowed because, for example, the original source reference was physically unavailable for inspection, these rules nonetheless require full details of the original source.
The value of sharing research by citation turns on accuracy in describing both the provenance and the content of the material cited.
The 85% claim badly needed to be masqueraded as high quality research – because it is literally incredible.
Eskew and Klink’s 2015 article in the Journal of the American Board of Family Medicine declared that unpublished work by post-baccalaureate students who studied a certain direct primary care clinic in 2013 “demonstrated” that the average fee for clinic members was 85% less than the cost-sharing paid by traditionally insured patients for the equivalent care. The 85% claim is preposterous.
The American Academy of Family Practice and affiliated groups regularly lament that 8% or less of health care costs are spent on primary care, and hold up 12 or 13% as an aspirational model. In 2013, the overwhelming majority of traditionally insured patients were covered by employer sponsored plans. These plans had an average premium of $5884 for a single adult and an actuarial value of about 87.5%, indicating average total health care costs of about $6725. Even if we apply AAFP’s aspirational 13%, the amount spent for primary care by insurers and insureds combined would be less $875. Reducing that by 85%, would mean that the direct primary care practice in question was receiving fees of less than $132 per person per year. That’s not credible.
As the NCSU students showed, however, the average member of the subject DPC practice paid fees of $473 per year. But, in that case, 85% savings would imply that primary care spending in traditional FFS practices was $3,153, about 47% of total health care costs. That’s AAFP’s aspiration more than tripled. That’s not credible either.
And then there is Katherine Restrepo, who gilded the 85% lily by assigning that huge reduction to total health care costs, not merely primary care costs. That would mean that the DPC patients had total health care costs of $1009 dollars. Subtracting the $473 they pay for primary care, that leaves $536 dollars for all downstream care. But for average FFS insured, even the aspirational 13% allocation for primary care leaves 87% for downstream care – $5851. Dividing $536 for downstream care of those DPC patients by $5851 for downstream care for FFS patients suggests that the Apex DPC’s patients saw a truly miraculous 91% reduction in downstream care costs. Nowhere near credible.
In a separate post, I explain that Restrepo’s suggestion that DPC office visits can be over four times as long as traditional office visits, is equally incredible. For now, keep in mind that Restrepo apparently expects the public to believe that DPC both has vastly lower costs and delivers hugely longer visits.
If you are a doctor choosing a pharmaceutical for your sister, feel free to rely on third-hand and fourth-hand reports of literally incredible results of unpublished pharmaceutical research by Master’s level students, some unnamed. If, instead, you are treating my sister, make sure you’ve paid your malpractice premium.
Please approach the design of healthcare systems that serve our brothers and sisters across the country with some concern for credible evidence.
“A university study found that patients treated in one Apex practice enjoyed average 35-minute office visits, more than four times longer than the average visit in a more typical practice. They also spent 85 percent less money.”
As discussed in a prior post, Ms. Restrepo is spinning more than a little bit in sourcing this information to a “university study”. In this post, however, we primarily address the substance rather than the provenance of her claim of four fold increases in patient visit times.
The work to which she refers on visit length is part of an unpublished course project by three post-baccalaureate management students from NC State: Ben Matthews, Chad Crafford, and Charles Queen. Mr. Queen has told me that only the 35-minute figure came from actual field research; the eight minute figure used for comparison came from one or more publications.
It is easy to find printed anecdotes about eight minute primary care appointments, frequently in the form of recollections from a physician explaining his migration to direct primary care. There are also diatribes about how all the time of a visit does not count when the doctor looks at a computer during some of the time during that visit. But there appears to be no published research that demonstrates that eight minutes, or anything approaching it, is the average time spent by the patient with the physician during an appointment at typical primary care practices.
Instead, there is fully documented and broadly accepted survey work from the professionals engaged by the respected Centers for Disease Control that shows that the average primary care visit around the period covered in NCSU work was 23.5 minutes. This measurement is essentially identical to that attributed in the AAFP’s Family Practice Management issue reporting on AAFP’s Family Practice Profile for 2015. That measurement would suggest that appointments at the Apex clinic are a bit under 50% longer than typical primary care visits. That’s still a feather in the Apex practice’s cap; it is also, as we will see, a fairly plausible outcome for an insurance-free practice.
What is not plausible is that any direct primary care clinic, even the one in Apex, actually delivers a four-fold increase in patient visit duration over traditional practices.
DPC advocates place their ability to deliver longer patient visits on their ability to reduce overhead. But how much overhead is there, and how much can it be reduced?
So, while a traditional practice would divide $100 of revenue into $60 of overhead and $40 for the practitioner, eliminating all billing and insurance would increase the funds retained by the practitioner from $40 to $53. That would allow an average physician to boost appointment duration by about one-third (1/3).
That boost would bring average patient visit duration above 31 minutes, a number that might reasonably taken as confirming the 35 minute visit duration determined by the NCSU students for the no-insurance clinic in Apex.
Still, pro-DPC activists regularly assign a much higher percentage of overhead to billing and insurance costs; at least one advocate suggests that as much as two-thirds (2/3) of overhead goes to billing and insurance. Let’s look at some possibilities that I’ve developed with the aid of a spreadsheet.
Assuming that half the overhead of a practice can be eliminated, then the amount of funds left for the practitioner would increase from 40 cents to 70 cents on the dollar. Doing so would let the practitioner spend 75% more time with her patients without a loss in revenue. And, while that might be a considerable achievement, it comes nowhere close to quadrupling visit lengths.
Even were it possible to eliminate all overhead, the effort would not generate visits that were four-fold longer. A practitioner who gets to keep 100 cents on the dollar instead of 40 cents can still only spend two and one-half times as long with her patients.
To spend four times as long with his patients, an average practitioner would have to reduce overhead by 200%. A physician would have to “keep” 160 cents on the dollar to get that result. Instead of the physician paying $32,000 per year for an assistant, the assistant pays $32,000 per year to the physician!
A physician could, one supposes, reach 160 cents on the dollar by increasing patient charges. So keep in mind that Ms. Restrepo asserts that the Apex practice manages, not only to quadruple normal visit times but, to lower patient prices by 85%.
In “Healthcare Innovations in Georgia:Two Recommendations”, the report prepared by the Anderson Economic Group and Wilson Partners (AEG/WP) for the Georgia Public Policy Foundation, the authors clearly explained their computations and made clear the assumptions underlying their report. The report’s authors put a great deal or energy into demonstrating that billion dollar savings could be derived from direct primary care under certain assumptions. After what I believe was careful examination, I concluded that those assumptions were unsupportable.
Here, I summarize my opinions, linking to about twenty individual posts. The posts themselves contain numerous supporting citations and data, as well as access to spreadsheets that can be used as templates for the reader’s own calculations.
AEG/WP made two questionable assumptions about direct primary care fees. One assumption was that appropriate direct primary care would have a fixed monthly fee of $70. My analysis shows that $70 lowballs the fee considerably. A second assumption was that monthly direct primary care fee would remain flat for a decade.; I noted that these fees were likely to track medical cost inflation. I recomputed the possible savings based on using a more accurate monthy fee and the same medical cost inflation number AEG/WP used. And I left in place AEG/WP’s assumption, discussed below, that direct primary cuts 15% off downstream care costs. Correcting only AEG/WPs two assumptions about $70 fees caused the billiondollar purported savings to fall by 85%.
The most central assumption in the AEG/WP analysis is that direct primary care reduces the cost of downstream health insurance by 15%. Direct primary care needs to show significant reduction in downstream care costs to justify the fact that even $70 direct primary care monthly fees would exceed expected fee-for-service primary care payments — by about $350 per year in the individual market. While the AEG/WP’s 15% assumption corresponds to a downstream care cost savings in the vicinity of $660 per year, there is no clear evidence to show that direct primary care can even cover its own $350 annual upcharge,
I contacted AEG/WP and learned the 15% assumption was based on three reports, available on the internet, about different DPC clinics. I was able, therefore, to carefully examine the information available to AEG/WP. In a single post, I addressed the experience of two clinics, which together were both the two largest and the two most current examples used by AEG/WP; I concluded that these both examples failed to address selection bias adequately.
I noted that the AEG/WP sourced low monthly fees to a set of direct primary care providers who had sharply lower fees than the providers to whom AEG/WP sourced its claim of downstream cost reduction. I suggested that an analyst seeking to establish cost-effectiveness would be well-advised to draw both cost data and effectiveness data from the same sources.
Not a penny of the savings in the AEG/WP report can be achieved unless direct primary care will significantly reduce downstream care costs. There is no sound evidence in the sources on which the AEG/WP authors relied that direct primary care can even manage to cover its own added cost, even if direct primary care were priced at $70 and would stay at the level for a decade.
May 2020: An important study by actuaries at Milliman now suggest that 15% downstream care cost reductions are credible, affect our previous take on the AEG/WP report.
KPI Ninja missed so many boats — a whole fleet really — in its Nextera report, you may wonder whether they come to analysis in good faith. Well, there’s one note in their favor.
Sometimes, KPI Ninja’s inability to make important adjustments disfavors their DPC client. In their analysis of DirectAccessMD, for example, an induced utilization adjustment would have favored their client. Good to know.
One might suppose that KPI Ninja is sometimes uninformed but mostly just eyes-deep in confirmation bias. But, surely, not bad faith?
Seriously, it’s not like KPI Ninja falsely told the world that the conducted risk score analysis on behalf of Nextera for a school district claims analysis in partnership with the Johns Hopkins’ ACG® Research Team or anything like that.
“Our statistically valid risk measurement was accurate enough to support our bragging when we say so, but not accurate enough to refute our bragging when anyone else says so. Although we used only about $10 million out of nearly $15 million dollars in claims data, rest assured that the missing millions can only reinforce our brags. Our DPC population had a 37% higher frequency of the conditions most likely to result in hospitalization, so our 92.7% reduction in hospital admissions is even better than it looks.
“Damn, we’re good.
“Benefit plans? What benefit plans? We don’t read no benefit plans! We don’t gotta show you any stinking benefit plan!”
One pet theme of most D-PCPs is, “Who can better determine quality better than my patient?”, a question invariably coupled to its speaker’s brag about a high patient retention rate.
And yet, in the Union County employee DPC clinic study, the actuaries observed a huge risk selection bias against the DPC, enough to require a 36% risk adjustment. Yet, the actuarial values of the competing DPC and FFS were very close to each other. So if cost did not drive the adverse selection made, what did?
A commonly given explanation is that older, sicker patients preferred sticking with their established PCP rather than being forced to choose between a small number of doctors working for the DPC clinic.
But does this not evidence that access to a larger community of fee for service doctors produced quality care? After all, who can better determine quality than those chronically ill patients who turned down DPC clinics?
Why would anyone expect that spreading the annual compensation of a primary care physician over one-third as many patient panel members would result in cost savings?
Why would anyone expect that finding a physician to give quality care that matches her needs among no more than two thousand direct primary care physicians would be more likely than finding quality care that matches her needs among the one hundred and fifty thousand who accept insurance?