Nextera: the KPI Ninja study in a nutshell.

The KPI Ninja report ignored the effect of an HRA for 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, certainly big enough to warrant assessment in connection with a $913 savings claim.

The KPI Ninja report failed to address the HRA. The report could be discarded for that reason alone.

But there’s more. Lots more.

The HRA issue, some closely related issues, and some more loosely issues, are discussed in more detail these posts:

KPI Ninja’s Nextera analysis: too many flaws to address them all collects many issues of the study’s design, data limitations, and its use of risk measurement. It’s long and detailed, because the deficiencies of the Nextra report are many and cut deep. (Latest important update was 10/27/2020)

Nextera’s Next Era in Cherry-Picking Machine Design focuses 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.

Nextera says these average people have an IP admit rate of 246/1k focuses on a single astonishing utilization claim from the Nextera report, which seems to reflect a severe error in basic data collection — one that just by itself would account for every penny of the claimed savings.

Explore these as you wish.

By some reckoning, this is the 100th post on dpcreferee.com.

Psalm 121, secular version.

I lift up my eyes,

Unto the mountains (of Colorado?)

From where will my help come?

My help will come from the Lord.

My help will come from the Board.

The union, maybe?


KPI Ninja’s Nextera analysis: too many flaws to address them all.

Candor matters.

Even as a cloud of tainted IP admission data looms over KPI Ninja’s analysis of Nextera’s SVVSD clinic, underneath that report there may well lie a pants-fire of misappropriated prestige.

“KPI Ninja conducted risk score analysis in partnership with Johns Hopkins’ ACG® research team [.]” KPI Ninja Nextera study, versions of 10.13.20 and 9.22.20, removed for later versions.

See also: “The case study came together though partnership with KPI Ninja and the Johns Hopkins’ ACG® research team.” From written description of Hint Summit talk by Nextera CEO. “[KPI Ninja] brought in the Johns Hopkins research that has significant expertise in what is called population risk measurement”. Nextera video presentation at Hint Health Meeting. “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 [.] “Nextera video presentation at Hint Health Meeting. Nextera slide from Hint meeting.

“We were not directly involved in this analysis.” Associate Director, HopkinsACG.org.

Silver linings matter.

Flipping the coin, let us give KPI Ninja and Nextera credit where credit is indeed due. They have disclosed, and at least attempted to address, the complete absence from the study data of both pharmacy cost data and employee/member cost-sharing data. These data limitations that have been present in some widely distributed self-studies by direct primary care firms, but are usually hidden or ignored.

Roughly 30% of the total health care spend is going to fall into that gap. That’s easily over $4M of data missing from the KPI Ninja study of Nextera’s SVVSD’s case, an amount that pretty much dwarfs the $580K that Nextera’s thinks it saved.

Still we will see that, despite having recognized these data gaps, the KPI Ninja report vastly underestimated how much resolving them might reduce Nextera’s money savings claims.

Nextera also stands tall for acknowledging the relevance of population health metrics to assessments that compare results between direct primary care popualtions and other populations. Words and concepts like “risk scoring”, “risk measurement”, “risk adjustment” or “selection bias” are rarely even mentioned in DPC firm self-studies. And, in its remarks to CMS on prospective payment models, the Direct Primary Care Alliance expressed a deep and burning hostility to the use of population risk data in assessing direct primary care providers.

Despite KPI Ninja having recognized the relevance of population health measurement, as we will see, there are significant problems in how KPI Ninja handled risk measurement and its relationship to other issues, including plan benefit structure and the structure of the study itself.

There are other problems in the report, as well. This post is long, because these flaws are numerous

Good on you, Nextera, for stepping up your self-assessment game. However, you still need to get things right.

IP admit rates matters.

Foremost, perhaps, is a matter discussed fully in the previous post. KPI Ninja reports an astonishing inpatient hospital admission rate for non-Nextera patients of 246 per 1k. That’s four times as large as the largest IP admit rate ever reported for ANY cohort in ANY study of direct primary care to date. This bespeaks a massive data error or massive cherry-picking or both.

Math matters.

Grade school arithmetic. For example, on that just mentioned issue of IP admit rate reduction, the Nextera report calculates the percentage difference between 246 admits per 1k and 90 admits per 1k as 93%. It’s not; it’s 63%. See report, p. 10, top table, bottom row. All four of the other entries on that row are wrong as well, mis-computing the percentage difference for urgent care visits, ED visits, OP admissions, and E&M visits. All five errors make the Nextera savings look better.

I recommend caution in regard to any number appearing in the Nextera report.

Study design details matter; KPI Ninja’s design distorts the single most important comparison — Nextera vs non-Nextera claims spend.

Third-party claim payments normally rise substantially over the course of a plan coverage year as plan members satisfy their deductibles or mOOPs. Accordingly, plans pay a significantly smaller portion of the claims of part-year plan members, particularly of newly employed enrollees. Many newly employed enrollees arrive too late to hit these landmarks before the end of their initial enrollment year. Year in and year out, third-party payers everywhere catch the part-year cost-sharing break.

Like other employers in the education world, the school district in the Nextera study sees a lot of new hiring in connection with the school year cycle, which begins roughly half-way through the benefit plan year.

Without a single word of justification, however, KPI Ninja made two unexplained, inter-related analytical choices for this study, in a combination that no certified actuary would have been likely to make. KPI Ninja elected to draw claim cost data for a mid-plan-year to mid-plan-year period that corresponds to a school year. But, at the same tome, they chose to open the study cohort to plan members who had been not been covered for an entire year’s claims cycle. In all, the average studied member had only 10.8 months of coverage. Even so, KPI Ninja’s unusual and unexplained choices might have been innocuous, if part-year members had been equally represented in both the Nextera and non-Nextera cohort.

Unfortunately, the representation of part year members is sharply higher for Nextera members than for non-Nextera members. The Nextera cohort members average only 10.1 month’s of coverage, the others average 11.1 months. No matter the reason for the skew, the school district catches the part-year enrollment break for about one-third of Nextera members, but for only about one-sixth of its non-Nextera members.

The upshot is that the reported employer paid claims for the two cohorts are not directly comparable. Some apparent “savings” result entirely from the oversize part-year membership of the Nextera cohort, a bulge that enters the comparison because of KPI Ninja’s cohort selection criteria. Had KPI Ninja used a study cohort selection criterion that address or avoids the over-representation of cheaper-to-the-district, part-year members in the Nextera cohort, a significant part of of the purported $913 spend gap would have vanished.

I estimate the appropriate adjustment would be solidly over $200 PMPY, based on an assumption of straight line growth of spend-to-date through the plan year. More important than anyone’s specific estimate is that KPI Ninja knew, or should have known, that the part-year members were present in higher proportion in the Nextera cohort but apparently never asked itself, “What difference does cohort selection make?” or “Can we eliminate any potential problem by using a slightly different study design?”

In their direct primary care report, Milliman Actuaries reduced this kind of problem by installing a minimum twelve month period of cohort membership. There are official Actuarial Standards of Practice that require actuaries address these kinds of situations, e.g., like Section 3.7.10 (b) of Actuarial Standard of Practice No. 6 from Actuarial Standard Board: “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.” See also, Sections 2.26 and 3.7.3 of the same standard.

KPI Ninja’s work on developing total employer spend that was literally substandard, and the result inflated the apparent cost-effectiveness of its client, Nextera.

Employee cost-sharing matters, Part 1. It counts as spending.

After admitting the data limitation of absent employee cost-sharing figures, KPI Ninja somewhat diminished concern about this $2,000,000 data omission by suggesting that its inclusion could only showcase additional savings for Nextera members. They even generated an example of how some missing employee cost-sharing data might be skillfully reconstructed from consideration of known costs, known utilization, and known aspects of benefit design. Specifically, they noted the Nextera plan members had no cost-sharing for Nextera’s primary care services. They then recalled the number of primary care visits by the Nextera cohort, computed an approximate $115 as the cost of a primary care visit from the claims data of the non-Nextera patients, and then applied cost-sharing information that they proudly announced had been “pulled directly” from the district’s benefit guide, to come up with about $12,000 in Nextera employee cost savings. Skillful!

KPI Ninja could have told us a lot more about employee cost-sharing, if it had the will to apply its skills to reveal matters both favorable and adverse to Nextera. Had they glanced around while they were looking in benefit guides, say while they were on the page previous to, and on the two pages following the one from which they “pulled directly” cost-sharing information that cast $12,000 in Nextera’s favor, they might have noticed that there was an HRA, not available to Nextera member. Had they “pulled directly” that HRA information and developed it, it would have cast $791,000 dis-favorably to Nextera.

The district picked up an average of $498 PMPY1 of first-dollar cost-sharing, only for non-Nextera members, through an HRA.

Or, if leaving the page from which they “pulled directly” $12,000 of Nextera gold was too much trouble, they might have noticed another vein on that page with well over $100,000 in pay dirt ready to be “pulled directly”, albeit in favor of the other guys. That’s because non-Nextera members paid only 10% in coinsurance, the district having doubled the coinsurance rate for Nextera members for the express purpose of mitigating some of the additional employer cost associated with Nextera.

Had the HRA and coinsurance cost-sharing information been “pulled directly” and then developed, the result have taken a $558 PMPY2 bite out of Nextera’s claimed $913 PMPY overall margin of victory.

By the way, each of the twenty or so Nextera mini-brags about individual cost categories ($1102 PMPY on ED visits! $594 PMPY on asthmatics!) would bear its share of the near million dollars cut-down.

1 Average district contribution PMPY to HRA computed by blending adult only payments and those for families with children, the latter being lower on a per person basis. I used the family mix data for the Nextera group, as it was the only one reported. Accordongly, this is a conservative estimate, because the Nextera population has proportionally more children. Even for adults with the full $750 HRA, the average contribution to the HRA fund is only about $600, an actuarial value that reflects that significant number of members with even lower claims totals than $600.

The average cost-sharing difference attributable to the district’s grant of deductible relief to non-Nextera members must, for a fair comparison, be capped by the average amount of deductible paid by Nextera members. That number is a bit harder to know with precision absent Nextera employee OOP data.

But it is a number that can be fairly estimated. Estimating an employee’s average liability under deductibles of various kinds and sizes is something health care actuaries do with great regularity. It is part, for example, of the process for determining actuarial value of insurance plans for metal status determination under the Affordable Care Act; CMS has a publicly available actuarial value calculator with relevant tools and tables. Similarly, Milliman Actuaries use Milliman Health Cost Guidelines (HCGs) based on national statistics; the tools MIlliman uses are also sold to analysts. While purchase of full HCG product is beyond my means, Milliman’s report on direct primary care serendipitously gave a glimpse of the HCGs in action that is more than sufficient for our purposes.

For inclusion in its landmark DPC study (Figure 12, line H), the Milliman team used the HCGs to compute the combined value to DPC members of the waiver of two separate deductibles. One applied to the first $150 of claims, the other applied to claims costs between $900 and $1500. Using the HCGs, Miliman estimated of the combined value of the two deductibles was $372 PMPY. Necessarily, a single deductible that covered a band from $0 to $750 would have a value of at least $372. The maximum value of a $150 deductible is $150; it follows that the minimum value of the deductible that covered the $600 band from $900 to $1500 was $222. But if a deductible in that band comes to 37 cents on the dollar, it follows that the minimum value of a deductible covering the band between $750 and $900 exceeds $55. The total of the minima for the three bands that cover the range of $0 to $1500 then, conservatively, declining to add even a penny of value for the band between $1500 and the full $2000 in deductible yields a floor of $699 PMPY in average spend subject to deductible for the direct primary care cohort. Accordingly, every bit of the $498 in HRA money represents costs that Nextera members pay and non-Nextera members avoid.

Again, KPI Ninja could have told us a lot more about employee cost-sharing, if it had the will to apply its skills to reveal matters both favorable and adverse to Nextera.

2 Includes HRA amount of $498 and coinsurance difference. 20% Nextera co-insurance calculated by dividing KPI Ninja reported Nextera employer claims spend by 4. The 10% non-Nextera co-insurance cost is given by the quotient of the non-Nextera claims spend divided by 9. The coinsurance difference works out to $60. The total is $572.

Here’s an example in table form of how employee cost sharing plays out for a single employee with over-all cost near average, but who needs a lot of primary care. So, yes, KPI Ninja is right that Nextera employees save on primary care cost sharing. But then Nextera employees are somewhat fleeced on downstream cost-sharing.

Employee cost-sharing matters, Part 2. It shapes employee utilization patterns.

The different cost-sharing landscape faced by Nextera and non-Nextera members not only shapes employees’ incurred costs, it also shapes incentives that effect whether an employee chooses to incur costs. Facing doubled coinsurance and going without $500 in HRA funds is quite likely to reduce “induced utilization” by Nextera members.

As expected in the case of high cost-sharing, the Nextera report indicates that a disproportionately large segment (20% ) of the Nextera cohort never seek downstream care. Even while admitting that it does not actually know whether ANY of these members actually visited a Nextera clinic for primary care, KPI Ninja suggests that this is the result of Nextera proficiency at reducing utilization. But there are decades of high-quality research, showing that cost-sharing differences have a large impact on utilization, while the Milliman study remains the sole high-quality, neutral work suggesting that the direct primary care model reduces overall utilization.

This is not rocket science. Nextera’s members have to pay more out of pocket for downstream care. Because they face higher out of pocket costs for downstream care, they use less of it.

To avoid having to pay more or to use less, informed patients who anticipate significant downstream care needs will tend to avoid Nextera and its adverse benefit design. Selection bias and induced utilization intertwine. Part of HHS’s risk adjustment process incorporates quantitative measures of utilization that take into account cost-sharing differences. More on this later.

The ACG® technical manual addresses the important connection between benefit design differences and ACG® concurrent risk measurement, the very type attempted by KPI Ninja. Milliman’s direct primary care team expressly noted that induced utilization should be considered in assessing the effectiveness of direct primary care.

KPI Ninja does not appear to have been acquainted with induced utilization.

Pharmacy costs matter.

As was the case with employee out-of-pocket costs, after honorably admitting the complete absence of pharmaceutical cost data, KPI Ninja dismissed concern about this data omission by suggesting that its inclusion could only showcase additional savings for Nextera members. No data was offered to support that proposition; the high point of support was KPI Ninja’s trust-me statement that its own, unidentified, and unpublished “research” showed that direct primary care presented opportunities for drug cost savings.

The opposite conclusion — that pharmacy data might well reveal additional direct primary care cost — is supported by actual evidence. A study comparing a direct primary clinic’s patient utilization measures against those of a propensity matched control population strongly suggests one successful DPC strategy is to reduce high expense downstream costs through increasing medication compliance. The DPC Coalition has even presented the clinic involved, an Iora clinic in Las Vegas, as a DPC poster child to the Senate Finance Committee; the cited work showed net overall savings for Iora’s members — along with, and very possibly because of, a 40% increase in prescription refills.

Indeed, both the current Nextera study and Nextera’s previous success brag suggested an important role for care plan adherence. Pharmaceutical spend data would certainly be salient, not only as important cost data, but as a way to help Nextera itself figure out how much, if any, of its value is the result of an implicit, de facto “Iora strategy”. If Nextera even vaguely tracks Iora’s path on pharma spend, it could easily bite off another $1043 PMPY from Nextera’s bragged savings.

The reason KPI Ninja has given for not using pharma spend data is, “We did not receive pharmaceutical data.”

Why the hell not?

3 Potential pharmaceutical cost increase for Nextera is conservatively estimated on the following assumptions: pharma spend is about 18% of all spending. The outlier adjusted employer only spend for Nextera members, excluding pharma spend, is $2360; implying a $518 pharma spend. Assuming that a partial “Iora strategy” entails only a 20% pharma increase, vs. 40% at Iora itself, results in a $104 increase.

Prescription information records the classes of therapeutic drugs used and supports risk assessment.

In addition to being salient on the costs issue, member prescription information gleaned from pharmaceutical claims reveals the classes of therapeutics used by each patient, which is reamrkably useful for risk measurement. In fact, the ACG® team strongly recommends that information be used in population risk assessment alongside age-gender-diagnosis based risk measurement of the type selected by Nextera. But the ACG® team also note that age-gender-pharma based risk adjustment can be credible even without diagnosis information. Equally aware as the ACG® team , the team from Milliman used an “age-gender-pharma” methodology rather than “age-gender-diagnosis” in their study of direct primary care, based on their assessment that the direct primary care model results in potentially distorted diagnostic data.

The reason KPI Ninja has given for not addressing pharma data is, “We did not receive pharmaceutical data.” With pharma data, KPI Ninja could have followed Milliman’s lead in avoiding the risk of distortion associated with diagnostic data in the DPC context.

Get the pharma data, please.

Risk adjustment matters; it needs to be done right.

Part 1. There is no indication that any raw data were actually adjusted for risk.

KPI Ninja computed the risk scores for the two populations at 0.358 (Nextera) and 0.385 (non-Nextera), a difference of about 7.6%. The norm for statistical work is to present unadjusted raw claims data, apply the computed difference to the raw claims data (regardless of its size) and, then present the adjusted claims data with a confidence interval to assist those using the data in making such judgments as they wish. As the Milliman report shows, this is done even when presenting differences deemed not statistically significant.

Instead of following standard statistical practice, the analyst declared the two populations comparable and then excused himself from actually applying any risk adjustment to modify the raw claims data. But 7.6% of health care costs is big enough to bother with. Nextera’s cost reduction is itself pegged at only 27%; when deducted from 27%, 7.6% gives a $256 haircut to Nextera’s brag.

Adding that $256 to the $558 in omitted employee cost-sharing differences cuts Nextera’s $913 savings claims down to $99 per member per year. If part of the reason for Nextera’s success is improving pharma compliance, it could it hit zero .

And even a Nextera $99 PMPY savings scenario turns on the accuracy of KPI Ninja’s calculation of a 7.6% risk differential. If that assertion is too low by a little over 3% Nextera’s cost savings claim is not just cancelled, it is reversed to a loss.

Part 2. There are sound 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 adjustment of about 21% is called for.

The mere 7.6% risk difference between the cohorts that KPI Ninja ran across (in its maiden voyage on risk adjustment waters) 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 young, but relatively sick, Nextera members coupled to a surfeit of old, but relatively healthy non-Nextera members.

That seems deeply implausible. Especially so, in light of a uniquely relevant report of heavy selection bias at on Nextera’s favor at Nextera’s own clinic, a report prepared and widely circulated by 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 whose headquarters was in the process of relocating from within three miles of the school district’s headquarters to a spot less than 20 miles more distant. 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 “Digital Globe” employees who chose Nextera had pre-Nextera medical claims that were 30% lower than the pre-Nextera medical claims of those who chose the non-Nextera option.

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? Cutting down a 30% selection bias by three-quarters? Really?

I will absolutely believe that is what happened when the Johns Hopkins ACG® Research Team tells me it happened.

Part 3. ACG® concurrent risk score comparisons, the type supposedly attempted by KPI Ninja in this study, are vulnerable to a bias that results from benefit design.

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, as needed, quantitative correction of risk measures by induced utilization factors.

One particular result of a benefit design artifact would be a discrepancy between 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.

Note: The entirety of the preceding discussion of benefit design artifact may be above my pay grade.

Part 4. KPI Ninja’s risk measurements rest on undisclosed and unvalidated methods that were purpose-built by KPI Ninja to increase direct primary care population risk scores. Anyone see a red flag?

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 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 depriving direct primary care practitioners of the ability to defend themselves against charges of selection bias.

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 analysis team. That highly-qualifed team ended up relying on age-gender-pharma risk measurement because, after carefully addressing the data donut hole problem, they found 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 appears — 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 validity of such synthetic claims lead the Milliman actuaries team 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 resources to attempt 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 final risk scores being manipulated by upcoding. There is no evidence that KPI Ninja’s secreted data development process, whatever it may be, includes any protection from deliberate upcoding by providers. Moreover, in a secreted process, upcoding may have been baked into the “Nextera Zero Dollar Claims (EHR)” cake, even if the baker had only the best of intentions.

In a sense, filling the data donut hole is a “good” form of upcoding, if done correctly. That’s a big if.

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.

As 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 the methodology has been validated, it is impossible to confirm that KPI Ninja risk measurement of the Nextera cohort has any meaningful connection to reality.

Any bona fide intellectual property in KPI Ninja’s proprietary methods can be protected even as it is made transparent and validated in precisely the same way ACG® protects its own intellectual property. KPI Ninja can show us the goods any time it wants to.

Here’s a simple plan for assessing risk levels in the two cohorts that we can use while we wait for KPI Ninja to reveal and validate its methodology.

  1. Run age-gender-only risk measurements.
  2. Get the pharmacy data.
  3. Run age-gender-pharma risk measurements.
  4. Compare.

If the clearly younger Nextera cohort is disproportionately sick and the older non-Nextera cohort disproportionately well, it should not be hard to spot. And, we would learn how well Nextera’s brags hold up under the transparent, validated age-gender-pharma approach taken by Milliman.

If you do not already know where my money is, read my prior post “Nextera’s Next Era in Cherry-Picking Machine Design.

In comparing chronic conditions costs, Nextera’s fees matter.

A table on page 12 of the Nextera report ostensibly shows various employer costs differences between Nextera patients and non-Nextera patients associated to a selection of sixteen chronic conditions. For six of these conditions, the shown costs are actually higher for the Nextera patients; there are thus report 10 “wins” and 6 losses. Kudos again to KPI Ninja and Nextera for reporting the losses, implicitly acknowledging the possibility that DPC may not have ALL the answers to EVERY chronic condition.

Some entries on the (page 12) chronic conditions table seem likely to reflect inpatient hospital admissions. These, as we have already explained at length in a prior post, appear to be vastly over-reported for non-Nextera members. For that reason alone, the reported chronic conditions table may well over-report non-Nextera members costs and, as a result, over-report the Nextera “wins” for ten chronic conditions and under-reports the extent of the six Nextera losses.

Again, as well, the chronic conditions report is based only on the district’s share of costs. Any complete account must include employee cost-sharing, likely to be much lower for non-Nextera employee by virtue of the aforementioned HRA and reduced coinsurance. Then, too, as an astute reader may have noticed, the table may also reflect induced utlilization. An HRA and reduced coinisurance may invite non-Nextera members to take better care of their chronic conditions.

Even more importantly, however, the table results are systematically skewed in Nextera’s favor, because the table has been built strictly from claims costs. For non-Nextera members, employer claim costs may flow from both primary care payments and downstream care payments. For Nextera members, there are employer claim costs only from downstream care.

But Nextera members receive primary care — some of the most expensive primary care in the world. Nextera’s subscription fees average $832 PMPY. Fair comparison of employer costs for chronic conditions requires a proper accounting of Nextera’s fees as part of the employer costs for chronic conditions. By leaving out Nextera’s fees, KPI Ninja joins a long, and sadly still growing, line of DPC cost savings analysts who somehow “forget” that DPC fees are costs.

Inclusion of Nextera’s fees would turn the table against Nextera substantially.

Giving methodological detail matters.

Despite being “a team of experienced professionals in clinical knowledge, public health, healthcare analytics, data modeling, academic research and software development, KPI Ninja has here presented extremely little technical description of its methods, data sources, underlying assumptions, data inclusion criteria, statistical description of the study results, and other important matter. Indeed, despite a full paragraph of wrapping itself in the “statistically validity” of the ACG® methodology, KPI Ninja did not supply a confidence interval for even a single statistic in its report.

Above, I have also addressed (a) KPI Ninja’s failure to describe its method for harvesting diagnostic data from EHRs, (b) their failure to account for induced utilization and (c) a cost analysis problem arising from KPI Ninja’s unexplained cohort selection decisions and their effect on cost comparisons.4

4The cohort selection problem may also have distorted the report’s many utilization comparisons. Recall that the average study member in the Nextera group had just over 10 months of coverage, versus just over 11 for his counterpart. Since KPI Ninja did not adjust for the part-year difference in its cost comparisons, it seems quite likely that KPI Ninja also failed to make the same kind of adjustments in its comparisons of utilization measures. Since KPI Ninja does not explain it calculations we are left to guess, for example, whether their counts of ED admissions for Nextera and non-Nextera cohorts were normalized for their respective 10 and 11 month years.

It matters. If the presented data on ED was indeed properly normalized, Nextera can fairly point to a reduction of 9%. If the data has yet to be normalized, then Nextera produces no reduction in ED at all. I can not say one way or the other whether KPI Ninja was aware of this issue, but I should not have to. It is the job of an analyst to anticipate issues that arise from the data presented and explain how they were handled. The cohort differences in part-year participation should have been weeded out by better study design, or addressed where they might have affected the reported study results.

Here are some other significant gaps in how KPI Ninja has set out the methods used in developing the analysis.

There is, for example, a glaring gap regarding an essential non-technical question about how KPI Ninja computed the employer’s spend for non-Nextera patients. KPI Ninja does not tell us whether, in addition to claims, its account of employer spend also included the nearly $800K the employer spent to fund the HRA. I certainly hope so, it seems so, and I assume so, but this is exactly the kind of thing that needs to be explicitly disclosed for the study to be fully and fairly evaluated.

Even when KPI Ninja gives its best appearance of having analytical chops, the lack of detail and discussion raises serious questions. So, for example, even as we again give kudos to KPI Ninja — this time for addressing the problem of outlier claimants, it is frustrating to see about 40% of total spend hacked away from consideration without being told either the threshold for member 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 of additional importance to know whether members incurring high costs were also excluded from the cohort in compiling utilization rates and/or in computing the risk measurements.

From the report itself, KPI Ninja appears to have identified a total of 11 outlier patients between the two cohorts. The analyst explains how, if included, a million dollar claim would heavily skew cost comparisons. They did not explain that, if the same million dollar member had daily infusion therapy, this could heavily skew KPI Ninja’s OP visit utilization comparison. They did not mention that, if the member’s million-dollar, daily-infusion woes were the results of a medical condition which ACG® scores with an exceptionally high risk value, KPI Ninja’s population risk measurement might also be heavily skewed.

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 risk. In their report, KPI Ninja addressed outlier members only in terms of their claims cost. There is no indication whatsoever that KPI Ninja appropriately accounted for outlier patients in its determination of either utilization rates or population risk. A good chunk of that astonishing IP admit figure for non-Nextera patients might have vanished had they done so.

Whatever the provenance of the data used, the ACG® system presented KPI Ninja with an array of cost adjustment models and parameters with which to assessing that data. This ACG® manual has 16 pages of tightly spaced text explaining its various models and the various considerations for selecting one rather than another. KPI Ninja eventually reported unscaled AGC® concurrent risk scores. But AGC has six dense pages that discuss they whys, wherefores, and pitfalls just of of different kinds of concurrent risk scoring factors — scaled and unscaled, with or without regression, age-gender only or with the diagnosis factors. All of the ACG® concurrent models were found to somewhat overestimate costs for those in the lowest risk quintile lowest risk risks and to somewhat underestimate costs for those in the highest risk quintile, and the size of that effect varies among models. Would that matter?

One hopes the crackerjack KPI Ninja team of “experienced professionals in clinical knowledge, public health, healthcare analytics, data modeling, academic research, and software development” had full command of all the relevant considerations for model selection and made a good choice.

The Milliman team explained how it made its choice of risk model in considerable detail. On explaining its choice of risk model, as on nearly every other methodological question, KPI Ninja explained bupkes.

Well, then.

The Nextera study misappropriated the prestige of a Johns Hopkins research team; has errors of arithmetic; made poor cohort selection choices that skew employer claims cost comparison, and perhaps utilization comparison as well ; excludes employee cost-sharing and pharma data; ignores that employee cost-sharing will skew heavily against the study’s savings claim; did not address induced utilization; relies on questionable risk measurement results from KPI Ninja’s rookie effort at getting them; relied on an unknown and unvalidated methodology for obtaining diagnostic codes; omits the employer’s cost for direct primary care itself when computing employer savings on chronic conditions; fails to disclose and discuss many other key details of the study methodology, e.g, the criteria for and the scope of outlier exclusion or how, and even whether, $800,000 in employer HRA costs were counted; and accepts without batting an eye a blended IP admission rate for the combined cohorts of a teacher-heavy school district employee and their families that exceeded by 30% the IP admit rate for Medicare patients who are three decades older.

Some members of the DPC tribe the KPI Ninja/Nextera school district report as a “thorough analysis”.

Please don’t stake the health of your employees on it.

As always, there is a comment section and a contact section. If you think I’ve erred, fire away. Better still, join the issues with me in a public forum.


Nextera says these average people have an IP admit rate of 246/1k.

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 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, then a $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 likely to have medical costs less than 40% of a those of a national reference data population. 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 country 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 of 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.

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 non-Nextera IP admission rates of 136 per 1K. 136/1k is still outsize IP admit rate for a commercial population; it is well over three fold higher than the largest IP admit rate for any cohort previously reported by Nextera’s own analyst, KPI Ninja. It would 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 told us that the Johns Hopkins ACG® Research Team found the difference between the populations to be statistically insignificant. So, let’s assume that’s true.

Even that 90/1k IP admission rate for Nextera’s own members stands 50% higher than 58/1k, the highest IP admission rate for any cohort, FFS or DPC, ever reported in DPC research. I’m not suggesting this proves that Nextera is worst direct primary care clinic ever studied. But by that criterion, it is no shining example either, and we can attribute Nextera’s 90 to 246 “win” on IP admit rates to Nextera excellence.

So, how did this FFS cohort come to 246/1k IP admits?

Does eschewing Nextera cause bad health luck — cancer, 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 page 10 data in KPI Ninja’s report, 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 7.82 admits for each of the 50 patients who had one or more admissions. That’s 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 as remarkable is how the DPC community so easily implies the inferiority of 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 claim a 93% reduction in IP admit rate. Warning the public that rejecting Nextera’s services could increases your risk of hospitalization by a factor of 13 is beyond reasonable commercial “spin”.

Apart from extreme cherry-picking, the most likely explanation would seem to be that the IP admit data — addressing a dominating stack of money in the school district’s health care cost universe — has been mishandled on a truly massive scale.

A reported 246 per 1k admit rate for any cohort of middle-aged workers and their children is just too bad to be true.

Did KPI Ninja or Nextera perform any “reality check”? Like asking the school district for IP admit figures from the year before Nextera came?

While I see some strengths in the KPI Ninja report, I also believe I have identified numerous other weaknesses that will raise the red flag even higher.

Meanwhile, as always, there is a comment section and a contact section. If you think I’ve erred, fire away. Could we do a public Zoom meeting on this?


Nextera’s Next Era in Cherry-Picking Machine Design

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 Nextera image above shows, $5,000 per year is about an average utilization level for an employee member of the district; an employee expecting average 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 – more than twice the average — might even hit the jackpot of shifting $1787 from his pocket to the employer, simply by rejecting Nextera. Heavier utilizers will do no worse than break even 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 utilitization. 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 — to suspect that a population risk-adjustment of about 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.

Update: October 22, 2020. So now Nextera has published an extended account of its SVVSD program. It’s here here.

(It was there before the Nextera’s claim that a Johns Hopkins research team had done the cherry-picking analysis was sent down a rabbit hole).

A video version, here.

I reply here and here.


HSA breaks for DPC defeat the purpose of HSA breaks

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: when the 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!


Helping those patients most dependent on DPC means defeating the DPC/HDHP/HSA “fix”.

Plus, two more reasons to reject the “fix”.

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 income to be able to save some. 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 does not draw insurance reimbursment or even 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 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.

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.


DPC subscriptions transfer financial risk.

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

A. Yes.

B. Yes.

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

A. Yes.

B. No.

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

A. No.

B. Yes.

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

A. Yes.

B. Many.

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?


A. Yes.

B. Yes.

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.


DPC cherry-picking: the defense speaks. Part 2.

Update: In the fall of 2020, KPI Ninja released the first study that relies on it’s new risk information technology. I find it sadly opaque.

Recap of Part 1

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. . . .

Luckily for us [Direct Primary Care doctors], we have nothing to do with this nonsense. [Emphasis supplied.]

ICD-10: It’s Nice Not Knowing You

Tell us how you really feel, Dr Gold.

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.

KPI Ninja Blog Post: Claims vs. EHR data in Direct Primary Care

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.

But how?

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

Update: In the fall of 2020, KPI Ninja released the first study that relies on it’s new risk information technology. I find it sadly opaque.

For completeness, two parting thoughts.

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!”


DPC cherry-picking: the defense speaks. Part 1.

Jump to Part 2.

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?

Link to Part 2.


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.

Link to Part 2.


Milliman: A $60 PMPM DPC fee buys an employer a zero ROI.

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.

So where will DPC advocacy turn?

Watch this blog!

* 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?


Downstream consequences when employers fall for non-risk-adjusted data brags.

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?

Union County Budget and Bureau of Labor Statistics

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 David Schwartzman for pointing out that in the employer insurance sub-market the rate of increase was higher. Kaiser Family Foundations 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 deliberatelty 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.


Medicare, dual coverage, and opt-out. The cherry on top of the cherry-picking machine for employer-based direct primary care.

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 new DPC brags failed to show bona fide risk-adjusted savings; together, they make clear that DPC brags rely on cherry-picking.

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.


No huge win for DirectAccessMD when employer DPC option data is compared with non-DPC cohort.

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.

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 far 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. And here is a copy of Dr. Purcell’s slide pointing out the DPC cohort of Anderson County employees was 2.7 years younger than the traditional cohort. Going back to the tape, I have computed that the expected overall medical costs for the DPC membership were 11.8% lower than for traditional primary care group. Subtracting that from raw savings of 14.1%, and a fair measure of the DPC savings for Anderson County is 2.3%, $9 PMPM, less than 20% of what Dr. Purcell has widely bragged.

The DirectAccessMD/Anderson County benefit plan made the direct primary care option more welcoming for riskier patients. That lead to a reduced level of selection bias and, accordingly, a reduced level of selection bias artifact masquerading as cost savings attributable to the direct primary care model. Even so, the amount of selection bias that remained amply supports adding the Anderson County DPC option “study” to the list of “studies” that have simply failed to support DPC brags of cost effectiveness.

I’ve referred elsewhere to my training as a scientist, which compels the engagement of any data presented that runs counter to a presented hypothesis. At some point in scientific history it became acceptable to stop listening respectfully to those who said the earth was flat.


On induced utilization in direct primary care, Milliman replied. I rebut.

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.]

What our study says about Direct Primary Care

I thank Milliman for putting a proper actuarial name on my concerns. I am not an actuary.

Even so, I dare say that the argument of that footnote looks a bit unsound.

As near as I can tell from a public document by CMS actuaries and some other sources, induced utilization adjustments in actuarial calculations, such as AV calculations for the ACA, are quite granular. The ACA methodology looks at various benefit design components (deductibles, coinsurance, copays, HRAs, mOOPs) and uses historical data to evaluate their impact on population cohorts at varying overall utilization levels. Induced utilization adjustments to the AV calculation emerge from that detailed analysis.

To see the fundamental wisdom of this granular approach, consider some variations on one known feature of the employer PPO discussed in Milliman’s study: a $750 HRA benefit. In the actual plan, a PPO member first pays a $150 general deductible; then is excused by the employer though an HRA from paying the next $750; and then faces a second “major medical” deductible of $600. Now consider moving the attachment point of the $750 HRA benefit down to $0 or up to $750. These three variations all have different effects on overall utilization. If the $750 HRA kicks in at $0, every claims-incurring member will benefit from it. If the $750 does not kick in until claims have-reached $750, certainly many and probably most PPO members in that particular employer’s plan would never have had access to that benefit. Just where “the rubber hits the road” matters, even though “in aggregate” there is the same amount of “rubber” in each of these three options.

The methodology of the Milliman footnote does not have this level of granularity. Instead, the authors deploy an unstated methodology to conclude that “the benefit design under the DPC option was slightly richer in aggregate than the PPO option”. It is not clear what “richer in aggregate” even means. What is clear is that the “in aggregate” model gives no account of exactly where the rubber DOES hit the road.

Consider adding just one bit of granularity. A key tenet of the direct primary care movement is that relatively cheap primary care is key to avoiding relatively expensive downstream care. For the PPO employees in the Milliman study, cost-sharing was applied to both primary care and downstream care. Although more modest “in the aggregate”, every penny of the cost-sharing burden for the DPC employees was directed at downstream care.

Then, too, we also know that at any level of overall utilization within the HRA window, the PPO group will face a zero marginal cost for upcoming increments of utilization of primary care or downstream care services. DPC members, at exactly the same levels of overall utilization, will have a marginal cost of zero for primary care needs but a twenty percent marginal cost for downstream care. We also know that the mean and median of overall utilization probably falls near the middle of the HRA window.*

Seemingly, a lot of cost-sharing rubber strikes right where it would be expected to lower relative utilization of relatively costly downstream services by mean and median members of the DPC group.

My instinct is that a disproportionate share of the pay-dirt of an induced utilization analysis is likely to be found in the most heavily populated utilization level cohorts — those near mean and median use. What does your gut say?

Given the complexities of each of two plans, and the sharp differences between them, determining an appropriately granular and valid adjustment for induced utilization might not be simple. Still, Milliman should have either performed that granular adjustment or, at least, it should have explained exactly how its aggregation model permits fair estimation without granularity. One or the other should have been done before the Millimian team insisted that it knew which direction an induced utilization adjustment would point.

*By Milliman’s own computations at Figure 12, lines H and I, almost 40% of the $750 HRA benefit went unused by the average PPO cohort member, while the average DPC member used less than half of the $750 value of the DPC-member deductible waiver.


Do bears sh. . .ake cherries out of trees? Selection pressure is built into DPC choices for any population with a normal deductible.

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.


The mixed bag of Milliman earns a final grade: B

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. But then, a failure to realize an important prerequisite for performing that isolation.
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 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

Page 46 of Milliman/Union County study

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 major ways, one of unmeasured consequence, and the other 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.

Milliman’s came up with a sound idea, but flubbed the design details.

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 both the DPC and FFS cohorts face 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 more detail on Milliman’s faulty attempt to reinvent the DPC cost reduction wheel, 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 yield a net utilization increase 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 available data about PCP salaries and primary care office overhead, probably 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 value, which resulted in a computed 12.6% reduction in overall usage.

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, 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 $96 dollars. 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 value of what the members received as $8 per month is absurd.

Milliman should amend this study, first accounting for the confounding effect of Union County’s cost-sharing scheme on downstream care costs, and then 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: 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.


Milliman’s valuation of DPC health care services at $8 PMPM rests on faulty 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.]


Slower-paced and longer visits use real resources. As do all the other elements claimed to generate DPC success, such as same day appointments, nearly unlimited access 24/7, extended care coordination. A principal justification for the subscription payment model is that too much of the effort required for comprehensive primary care escapes capture in the traditional coding and billing model.

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 case study, $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 market price 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.


ATTN: Milliman. Even if Union County had not waived the $750 deductible, the County still would have lost money on DPC.

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 it actually 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.


Risk adjustment, and more, badly needed for KPI Ninja’s Strada-brag

Amended 6/26/20 3:15AM

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.

Challenge accepted.

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.


For Qliance, a plausible net savings is 6.8%

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!

Letter, DPC Coalition 1/26/2016 Despite removal of the ER visit claims data, the Coalition version of the table continues to represent that it contains all-claims data, and has at least one error of arithmetic.

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:

Google Spreadsheet

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.

Milliman study for the Society of Actuaries

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%.


DPC is uniquely able to telemed: a meme that suffered an early death.

An update to this post.

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.

Larry A Green Center and Primary Car Collaborative.

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 Nextera/DigitalGlobe study design made any conclusion on the downstream effect of subscription primary care impossible.

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.

UPDATED JUNE 2020. Here’s a link to an earlier version.

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.

Nextera Healthcare + DigitalGlobe: A Case Study

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 be a nice 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 care costs.

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.


Why is subscription DPC the precise hill on which self-styled “patient-centered” providers have chosen to make a stand?

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 Direct Primary Care in a nutshell.

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?

Union Countu Budget and Bureau of Labor Statistics


DPC Alliance manifesto steps on its own foot attempting to prove that DPC saves money.

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:

  1. FFS primary care practice is being destroyed, financially, by the Covid-19 pandemic.
  2. DPC is thriving, financially.
  3. DPC has always been great, and has always been superior to FFS.
  4. Because of the pandemic, DPC is now even greater and even more superior to FFS.
  5. 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.
  6. DPC achieves lower overall healthcare spending.
  7. 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“.

Ah, yes, the footnotes.

Here’s Footnote 5:

Basu, Sanjay, et al. “Utilization and Cost of an Employer-Sponsored Comprehensive Primary Care Delivery Model.” JAMA Network Open, vol. 3, no. 4, 2020, doi:10.1001/jamanetworkopen.2020.3803.

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”).

It is also the only DPC plan to date (May 2020) that has received extended, comprehensive, risk-adjusted analysis from an independent team of actuaries. They found that:

… [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.

Pleaase click here for further detail..

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%.


dpcreferee’s 2017 op-ed on Union County’s failure to save with DPC proved to be almost spot on.

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 team of health care actuaries from a firm of highly regarded actuaries known widely for its health care work. The study was prepared for the Society of Actuaries. See discussion below my op-ed.

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 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.

https://www.soa.org/globalassets/assets/files/resources/research-report/2020/direct-primary-care-eval-model.pdf at page 7.

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 typo

As recorded in the quotation just above from page 7 of Milliman report, Milliman found that introduction of a DPC option increased the employer’s expenses by 1.3%. Page 7 is part of the report’s executive summary. In a discussion section at page 46, however, the same report states that the introduction of a DPC option reduced costs by an unstated amount. How can this contradiction be resolved?

The data and computations for computing over all costs are presented in Figure 12 on page 32, its key on pages 33 and 34, and a discussion on 35. These make quite clear that the average ROI estimated by Milliman was indeed a loss of 1.3%. Figure 12 is set out below.

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.


That “DPC is working while FFS is failing financially because of COVID” meme takes a big hit; proof furnished by DPC Alliance.

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:

  1. FFS primary care practice is being destroyed, financially, by the Covid-19 pandemic.
  2. DPC is thriving, financially.
  3. DPC has always been great, and has always been superior to FFS.
  4. Because of the pandemic, DPC is now even greater and even more superior to FFS.
  5. 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.
  6. DPC achieves lower overall healthcare spending.
  7. 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”.

The Alliance also linked a breakout focused on DPC practices. 52% of PCP in direct primary care practice responding to the same survey expected to seek such loans.

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.


Why a policy wonk like Wyden might (and, perhaps, should) kill a DPC/HDHP fix for subscription medicine. Short version.

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. See this in-progress, longer version of this post.

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.

Dworkin, Roger B. (1973) “Death in Context,” Indiana Law Journal: Vol. 48 : Iss. 4 , Article 6.
Available at: https://www.repository.law.indiana.edu/ilj/vol48/iss4/6

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.


The “DPC is uniquely able to telemed” train has left the station. Everyone is telemeding now.

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.

Have a look at this for example:

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. ]


DPC + Cat is not a good substitute for full ACA Medicaid expansion

Adapted from B. Matthews, C. Crafford, and C. Queen, Direct Pay Medical Model at Access Healthcare. Presentations of a course project at Poole College of Management, North Carolina State University, Chapel Hill, NC, August 23 & September 13, 2013.

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.

  1. Adjust Forrest’s patients’ cost curve upward so it no longer excludes downstream care costs born by real patients;
  2. 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;
  3. Adjust the curve of the United Sates downward so it reflects the non-Medicare population and excludes long-term care expenses;
  4. Give the correct upward curving form to Forrest’s patients’ cost curve; and
  5. Viola — Forrest’s patients’ cost curve will look a hell of a lot like everyone else’s.

The only bona fide university study of DPC has a message: “There’s no data.”

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.


Spin doctor says DPC saves 85%. Don’t bet on it.

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.


Can you imagine that?

Did Restrepo imagine it?

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.


Spin Doctor: DPC office visits are four times as long as PPS office visits. Don’t believe it.

“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.” 

Kathlerine Restrepo, John Locke Foundation press release of March 22, 2017

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?

A 2014 quantitatively detailed, peer-reviewed academic study of “Billing and insurance-related administrative costs in United States health care” concluded that billing and insurance-related costs in physician practices amounted to thirteen percent (13%) of gross revenues. This works out to be about 22% of the estimated 60% overhead expenses (see here and here) for family practice physicians. 

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%.


A single-post critique of AEG/WP’s recommendation on direct primary care.

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 billion dollar 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 noted that AEG/WP supported its 15% assumption only by referring to undisclosed research internal to its own team. I noted that the marketplace had already demonstrated skepticism about similar claims. I noted that a DPC practice founded by one of the authors of the AEG/WP reports authors had made similar claims, without producing supporting data. I further noted that selection bias had infected the best documented argument that direct primary care reduced downstream costs.

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.

The third example, the Nextera clinic, deserved its own posts. Their report noted obvious selection bias, while revealing modest evidence that Nextera cuts cost for some of its patients. But the data set was skimpy and contaminated by results for fee for service patients. The patient data that showed downstream cost reductions for patients served by Nextera included both significant numbers who paid only Nextera’s fixed monthly fee and significant numbers who paid Nextera only on a per visit basis. This may be an adequate method for measuring the positive value of Nextera. It is hardly sufficient as a yardstick for the positive downstream value of fixed-fee direct primary care. In a separate post, I noted that Nextera’s experience showed only a $72 PMPM overall claims cost reduction, an amount that would barely exceed the $70 monthly fee.

I pointed out that even if the foregoing criticisms of the source data on which the AEG/WP relied were in error, their further assertion that the 15% assumption is a conservative one is incorrect.

I also pointed out that AEG/WP’s source material for the 15% assumption consisted only of marketing information, and I suggested that a few brags from a few DPC companies is not a sound basis for public policy decisions.

I spoke with the actuary who was on the AEG/WP team; he made clear that his role did not include validating the 15% assumption.

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.

Here’s a chronological list of posts relating to AEG/WP’s “Healthcare Innovations”.

DPC from 30,000 feet, on September 2020

  • Even with all the overhead reductions that come from not taking third party payment and/or from not billing on a fee for service basis
  • Even with those reductions transformed to increased primary care access that results in clear reductions on ED visits and other urgent care needs
  • Still, Direct Primary Care with panels of 600 patients per PCP, priced at $75/adult/$40/kid per month, does not financially outperform traditional, third-party paid, fee-for-service based primary care. (Search “Milliman” in this blog.) (Maybe 2-3%. Search “Anderson” in this blog.)
  • That $75/$40 is what it takes for a 600 patient panel to support a single PCP at average family physician compensation.

In other words, the DPC model has not miraculously enabled PCPs to “punch above weight” overall. Subscriptions for 38 minute visits, loosely scheduled to allow same day visits, and facilitate 24/7 access end up costing about as much as the existing FFS alternatives.

Perhaps, the whole DPC package makes the patient healthier in some way that does not show up as a financial savings. This is unproven. But DPC docs have not been content with one unproven overall claim. From its beginning, DPC has insisted on two unproven claims: better overall health and lower overall costs. The most clear recent emphasis has been on lower cost. The most clear recent evidence has been to the contrary.

DPC is way different than you paying Neflix. Notes

The State of New York has the financial capital of the country (arguably the world), has the most insurance companies in the country, and was the biggest state for the longest time. For these reasons it is generally looked to for leadership in the law on financial subjects primarily governed by state law. Here’s their statute.

(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 state has the idea of fortuity, contingency, unforeseeability, something substantially beyond the control of the parties.

Many states, probably most, do indeed regulate service contracts/extended warranties for home and automobiles as insurance. Many regulate prepaid legal services as insurance.

Not Netflix, because it is sold on the basis that the subscriber’s utilization level is substantially within the subscriber’s control.

I can watch every night as I wish. For a DPC visit, I need to have a medical need to attend.

Other ways in which Netflix differs, from a policy perspective.

Netflix expressly reserves the right to change permitted utilization and pricing at any time for any reason, can vary server capacity, can vary program quality (its payment for licenses probably depend on many times a show is streamed).

DPC has explicit and implicit guarantees of quality and quantity, there’s a professional standard for determining need for a visit and quality of what has to be performed.

Marginal costs for Netflix supply are low, inputs are readily expandable, high utilizers have at best modest effect on supply or quality available for synchronous use by others; significant economies of scale; elastic supply.

DPC visits by patient X fully excludes patient Y from synchronous use (or MD from golf course); high marginal cost; supply vastly less elastic.

Way different social value for failure of the vendor to deliver.

Netflix: disrupted video streaming

DPC: disrupted access to health care

Netflix server outage in Seattle: 20K viewers each spend 5 minutes switching to Hulu

Qliance closes doors: 20K patients in Seattle hunting for PCPs accepting new patients; not getting med refills or timely A1c; trying to add health care plans outside of enrollment periods.

Systemic effects: If Netflix diverts TV addicts from Hulu, who cares. If DPC diverts a relatively healthy sub-population from, say, ACA compliant individual market policies that are guaranteed issue that would make ACA guarantees for those with pre-existing conditions more expensive.

If Netflix usage was essential to life and if the need for Netflix usage surged owing fortuitous viral infection, it might be wise to regulate Netflix

Finally, in considering whether or not DPC should be regulated as insurance, consider a DPC that finds itself with 50 spaces to fill and has to determine whether to make a pitch at/to

(a) an architecture firm full of middle-class and up college graduates working mostly at home this coming fall or

(b) a small meat-packing firm full of low income low education folk working indoors in close quarters.

Then consider DPC writ large, with clinics competing for business. Reread the foundational works of health care economics, and tell me why DPCs won’t end up as a primary care microcosm of underwriting, cherry-picking, death-spirals, and all of that.