Sunday, November 8, 2026

Chris Klomp: #2 at HHS, Update on his Views and Priorities for CMS

HEADER.  Chris Klomp is about to become the official #2 at HHS - the Deputy Secretary.  How does Klomp think Medicare should pay for innovation? His recent WSJ interview and two Senate hearings offer revealing answers: reward outcomes, make clinical AI reduce costs, and align FDA and CMS evidence expectations earlier. But the details matter: RAPID excludes laboratory tests, and senators challenged AI-assisted prior authorization. Our deep-dive AI review examines and connects all three appearances to explain an increasingly influential HHS official’s priorities—and what they could mean for diagnostics, biotechnology, and reimbursement.

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AI CORNER
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In March, this blog featured several articles about Chris Klomp, a senior CMS policymaker who was being moved to a #2 position under RFK Jr at HHS.  A Senate committee recently moved his nomination for Deputy Secretary forward.

What's new for him?  WSJ just published a 25-minute interview between Klomp and Liz Whyte.  Find it here.   We cover that first.  Then, in a second section, we provide highlights from his Senate appearances in September.

Across all three appearances, Klomp returns to several consistent themes:

  • Reward outcomes rather than activity. The hearings emphasize shared savings and appropriate preventive spending; the WSJ interview extends this argument to clinical AI and explicitly cites ACCESS.
  • Accelerate innovation while preserving standards. September 15 highlights FDA–CMS evidence alignment; September 16 emphasizes inspections and development timelines; WSJ broadens this into faster trials and biotechnology competitiveness.
  • Lower drug costs without weakening American biotechnology. He repeatedly presents these as simultaneous obligations.
  • Modernize government technology. The hearings focus on aging Medicare systems; WSJ emphasizes interoperable data as essential infrastructure for clinical AI.
  • Manage through trust, competence and accountability. These leadership principles recur almost verbatim.

The strongest continuity is his belief that better incentives and better execution can improve access while restraining costs. The WSJ interview develops that philosophy more expansively; the hearings expose the difficult implementation questions.

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Saturday, November 7, 2026

AI Guest Faculty: Lessons from 1870s for Growth of Genomics Today

 What can the rise of Germany’s synthetic-dye industry in the 1800s teach us about American genomics today?

Johann Peter Murmann’s fascinating historical research, followed by a 2016 computer simulation, offers an unexpected starting point.

My new white paper explores how scientific talent, capital markets, and Medicare reimbursement shape industrial winners and losers. One intriguing question: Would genomics have evolved differently if Medicare coverage had been fragmented among 20 independent regional payers?

A historical comparison—and a proposed AI-assisted experiment.

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Executive Summary

Why do some genomics companies become industry leaders while others, sometimes equally innovative, struggle or disappear? The answer may lie as much in institutions and financing as in scientific discovery.

This white paper draws an unexpected historical comparison with the nineteenth-century German synthetic-dye industry. In his 2003 book, Johann Peter Murmann examined how universities, scientific talent, patent laws, and industrial competition helped Germany achieve global dominance. In 2016, Murmann and Thomas Brenner used computer simulations to test these historical explanations, finding that universities' responsiveness to industry's demand for chemists was particularly consequential.

The paper proposes applying a similar approach to the evolution of American genomics, with special attention to Medicare reimbursement and MolDX.

One largely overlooked institutional feature deserves scrutiny: a centralized laboratory can receive traditional Medicare reimbursement for testing beneficiaries nationwide through its own Medicare contractor. What if Medicare had instead been divided among dozens of independent regional payers, each making separate coverage decisions?

Such fragmentation might have profoundly altered the industry's financing, growth, and competitive landscape.

The paper outlines an AI-assisted research program combining company histories, reimbursement decisions, financial data, and computational simulations to investigate these possibilities.

The objective is not merely to explain which companies succeeded, but to understand how America's institutional environment helped shape their success.

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DOWNLOAD THE 10p PDF WHITE PAPER... HERE.



Friday, November 6, 2026

Three Companies: Investor Calls for Tempus AI, Caris, and Natera, Quarter to Quarter

Tempus, Caris, and Natera see enormous opportunity in genomics—but reimbursement determines the pace. Our new AI-generated white paper compares November earnings calls with prior-quarter expectations, examining MolDX coverage, payer pricing, clinical evidence, and promises still awaiting payment.

Here's a summary:

  • Tempus AI, Caris, and Natera report strong demand for genomic testing, but reimbursement determines how quickly new products become sustainable businesses. Comparing November earnings calls with the prior quarter reveals distinct strategies. 
    • Tempus anticipates substantial oncology pricing gains while pacing MRD expansion around coverage.
    • Caris expects higher liquid-biopsy reimbursement to help finance new monitoring and detection products.
    • Natera is converting clinical evidence into broader coverage, including a new MolDX indication for liver cancer surveillance. Several earlier expectations remain unresolved, particularly payment for emerging applications and Japanese commercialization.
      • [Steve Chapman steps down as CEO - here.] 
  • Across the companies, the key distinction is between technical performance, clinical utility, coverage, and actual collections. Competitive advantage increasingly depends on generating persuasive evidence and financing the long interval before reliable payment.
Find the 15-page report in the cloud  HERE.


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Why a Strong Diagnostic Test May Add Little New Information

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Why a Strong Diagnostic Test May Add Little "New" Information

[Chat GPT 6]

My colleague Luis Cano recently posted a useful example of a question that diagnostic developers should ask much earlier: What does the new technology add to what we already know?

His post described a pancreatic neuroendocrine tumor preprint involving 1,438 patients and approximately 30,000 histology images. An AI model extracted prognostic information from routine H&E slides.  

Yet, as Cano reported, a model using established clinicopathologic information achieved a C-index of 0.875; adding the AI increased it to 0.883. [Sivasubramanian]

The AI could predict recurrence. The clinical information could already predict recurrence very well. Those findings can coexist.

My comment on his post drew on a lesson I remember from MBA statistics. Given an outcome and ten candidate variables, variables A, B, and C might predict the outcome extremely well. Then variables D, E, and F might do almost as well. That does not mean we have discovered two separate reservoirs of predictive power that we can simply add together. We may have found two ways of measuring much of the same thing.

For molecular diagnostics, digital pathology, and AI, this distinction can separate an impressive publication from a compelling case for clinical adoption.

The arithmetic works but the attribution changes

We expect addition to be commutative: 3 + 5  =  5 + 3. It is tempting to assume that combining predictors works the same way.

An important qualification makes the lesson precise. Ordinary correlation is symmetric: the correlation of X with Y equals the correlation of Y with X. And if we fit the same ordinary regression model to the same data with the same predictors, simply rearranging the columns does not change its final predictions.

  • What depends on order, is the incremental credit we assign to each source of information.

“How well does this test predict recurrence?” and “How much does this test improve prediction after we use the pathology report?” are different questions.

The first measures performance in isolation. The second measures added value against an existing baseline. A test can perform very well on the first and make only a modest contribution on the second.

Here, “clinical first” means choosing the clinical model as the benchmark. 

Thursday, November 5, 2026

The Genomics Consulting Book Guide to CLAUDE

If this looks familiar, I posted at Linked In  about November 1, but not here at the DIHP blog.

They Infest Linked In

Lately, every day Linked In has been offered me "McKinsey Guide to CLAUDE," "Consultant's Guide to CLAUDE."  Usually the exciting-sounding offers dead end at a registration page and sign in with your email.



Steve Jobs Got His Best Ideas Walking, Too

Walking home from lunch with some consultant colleagues, it occurred to me that my own Claude account could probably write me a guide to strategy consulting in ten minutes. It did. And honestly, it’s pretty good.

As I read, I kept thinking: “I can use THIS to help Client A today—and THAT with my deck for Client B tomorrow.” So I’m sharing it here. Free. No registration or email required.

What You Get (for free)

It’s a 44-page booklet in two parts.

The first 17 pages introduce 21 consulting skills—from framing a problem and auditing assumptions to pricing strategy, competitive war-gaming, and writing a decision memo. Each includes a reusable instruction you can paste into Claude, plus a worked example involving a fictional genomics company facing major strategic decisions.

Pages 18–44 start over and take that same company through a detailed consulting engagement. You see how the skills connect, how one finding changes the next decision, and how the analysis becomes a recommendation for the board..

It’s a little “MBA in a Box”—with a genomics startup as the classroom.

DOWNLOAD IT HERE.


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In the case study, nearly every fact is labeled with its origin.  S, sourced from a "data room" (a list of key facts), D (derived from such facts, such as revenue - cost = profit [D]).  Or A, assumed, which you'll find why in that sentence or two. 

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This source offers a booklet of 21 Claude Consulting Skills based on email registration and temporary access.  I'm dubious how great it is.  I bet you can do better.  Please use Fable t5.1 medium to simulate this booklet (see attached) and its 21 topics where Claude Fable can simulate real consulting teams and outputs.  The attachment is the cover page - simulate the booklet.

This was my initial prompt, with some screen shots of covers. It came back with a book, 16pp,  7500 words.  It offered to expand with a detailed business school type case study; I said yes. Claude then basically doubled its length which produced the 44 page work product.  Claude and I spent perhaps an hour with back-and-forth refinements.  

The case study, which Claude made up quickly while writing its deliverable.Meridian Genomics is a fictional 120-person CLIA lab in Phoenix with a tumor-informed MRD assay for colorectal cancer. It has a PLA code but no Medicare coverage, $14 million in revenue, a $6.2 million annual loss, and $22 million in cash. The board wants a three-year plan and has mostly decided what it should say: get MolDX coverage. Management’s plan is to submit in early 2027, expect coverage a year later at about the $1,900 rate the dominant national competitor reportedly receives, and triple commercial volume by 2029.

The first twist comes from arithmetic on the client’s own numbers. Meridian’s fully loaded cost per test is $2,100. Its commercial tests bring in $1,522, so each one loses $578. Coverage at $1,900 would still lose about $200 per test. The board’s strategy would turn a flat, money-losing channel into a growing, money-losing channel. The real business is biopharma. Trial sponsors using the assay as an endpoint account for 68% of revenue, grew 40% without any sales effort, and earn about $694 per test. The board had been treating that business as a bridge to coverage.

The second twist is in the profit pool. The money in MRD isn’t in the test fee. It sits in trial services, meaning documentation, logistics and regulatory support, and in licensing longitudinal data. Meridian already does trial services but has never charged for them as a product. The national competitor’s low-margin covered volume turns out to be a way of building the data asset.

The recommendation is to make trial services a named business line with per-program pricing, data-rights clauses and regulatory-grade documentation, and to cut cost per test toward $1,400. Meridian would prepare the coverage dossier but submit it only after passing gates the board sets: cost at or below $1,500, a full sponsor pipeline, and a realistic MolDX timeline. In the base case this is worth about +$19 million in NPV, compared with roughly −$2 million for cost-then-coverage. The downside is mild under this plan and could eat most of the balance sheet under the coverage-first plan.

The third twist comes from the war game. It breaks an earlier assumption that the competitor would take 12 to 18 months to respond. In the simulation it offers sponsors free assays in exchange for data rights within a year. Meridian can’t beat free on price, so it retreats to registrational trials, where documentation and exclusivity are worth more than a discount. That revision then flows back into the segmentation and the roadmap.


Wednesday, November 4, 2026

CMS Pulls Algorithmic Lab Tests Off the CLFS: The CY2027 SaMS Rules, Decoded

 

CMS Pulls Algorithmic Lab Tests Off the CLFS: The CY2027 SaMS Rules, Decoded

Wednesday, November 4, 2026

AI CORNER [New replacement article, Claude, issued Nov 9]

OPPS final rule:

https://www.federalregister.gov/documents/2026/11/06/2026-22739/medicare-program-hospital-outpatient-prospective-payment-and-ambulatory-surgical-center-payment 

PFS final rule:

https://www.federalregister.gov/documents/2026/11/09/2026-22743/medicare-and-medicaid-programs-cy-2027-payment-policies-under-the-physician-fee-schedule-and-other 

Read here or download as 12p PDF report.



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A Big Change - Suddenly It's Here

The CY2027 OPPS and PFS final rules contain a policy that looks like housekeeping. It isn't. CMS has decided that a stand-alone algorithm run on lab data that already exists is not a clinical laboratory test at all. That covers a genome re-analysis, an AI read of a digitized H&E slide, and a classifier applied to an RNA transcriptome. Each is now an "other diagnostic test," a cousin of the AI-read CT scan, and starting January 1 it gets paid like one.

For 2027 the dollars barely move. Almost everything else does: who pays coinsurance, which date-of-service rule applies (none of the lab ones), how new codes get priced (nobody quite knows), and whether a code that includes staining a slide counts as "software" (it depends). Here's the tour.

The Short Version

CMS finalized 14 CPT codes (up from 10 proposed) that leave the Clinical Laboratory Fee Schedule. Under the OPPS they move to new technology APCs with a brand-new status indicator, O1 ("Software as a Medical Service"), which flags them as software and pays them separately. Under the PFS they get national pricing with procedure status A (active). Payment is crosswalked from 2026 CLFS rates and rounded into the fixed new technology payment bands, so the prices are familiar.

The surroundings are not. These services now carry ordinary Part B cost-sharing. They escape the laboratory 14-day date-of-service rule. And they sit inside payment systems built on budget neutrality and, perhaps eventually, packaging. CMS corrected its code list after comment (two codes out, two in, one long-deleted code dropped, two moved up a payment band), but it gave up nothing on principle. It has said where future algorithmic lab codes will be paid, but not how they will be priced when they arrive.

There is also a true philosophical oddity underneath the rulemaking for SaMS. Physical work does not disqualify a service from the SaMS category (it is almost welcomed), unless that physical work happens to be laboratory work. More on that below. 

Why Some Genomics Labs Reach Financial "Escape Velocity" - But Many Don't

Recently, Merck KGaA annnounced it was acquiring BioTechne for over $11 billion dollars.  BioTechne's management has been strong - that's up from a $5B valuation about ten years ago.  So the C-level team knew what it was doing.

But, if you look through a genomics lens, along the way the same BioTechne C-suite bought Exosome Dx for $325M (2018) and sold it  for $15M (2025).   Why was it so difficult to forecast the value of the Exosome business - by twenty-fold (5%)?

We discuss in a new white paper why the financials of the genomics industry can be so noisy, and often for reasons that will be missed by outsiders.

Our paper includs a humous appendix of real statements from genomic SEC documents.  Like, 'We may get paid rarely, if ever."

Find the white paper in the cloud HERE.

Tuesday, November 3, 2026

Puzzling PAMA Pricing #2: Labs & Losses Deep Dive

 You've heard about how PAMA affects different groups - big labs, small labs, physician labs, and the labs of different medical specialties.

Here, I used the assumption that PAMA pricing was pushed all the way - that tomorrow's prices were set at the actual medians found in PAMA data.   

My assumption was, we'd see a normal or Gaussian distribution around the average price cut.  But no.  For the whole population of providers (with >$50,000 Medicare lab income), the distribution of losses is very irregular.

I divided these into four groups:

  1. Group 1, Winners (N=82+80)
  2. Group 2, Average (N=1758)
  3. Group 3, Losers (N=984)
  4. Group 4, Big Losers (N=15+123)
Here's the view, click to enlarge:

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AI CORNER
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Four Populations, One Fee Schedule:
Reading the Fully-Phased PAMA Impact

Nov 3, 2026 · @Bruce Quinn

Introduction

Suppose PAMA's private-payer medians were applied to Medicare lab pricing in one stroke, with no 15%-a-year phase-in and no further congressional delays. The intuitive answer is "everyone takes about a 16% haircut," which is the aggregate figure CMS has reported. The intuitive answer is wrong. Line up every provider with at least $50,000 in 2026 Medicare lab revenue (4,855 NPIs, about $5.8 billion in baseline payments), sort them by fully-phased percent change, and you do not get a bell curve. You get two tall peaks, a small cluster hugging zero, and a tail of providers losing 40% or more.

The $920 million aggregate cut works out to 15.8%, almost exactly what CMS said. But that average describes almost nobody. The histogram is a distribution of code portfolios, and code portfolios sort by specialty with remarkable discipline. This post walks through four groups descriptively, then asks, as a microeconomist would, what is really going on in each.

One housekeeping note: the rows are NPIs, not consolidated companies. That turns out to be a feature. The same corporation can land in three groups under three NPIs, and that tells you something.

Orientation: The Shape of the Distribution

The histogram uses fifteen four-point bins from −46% to +6%, with edge bars catching anything beyond. Two spikes dominate. The first, at −18% to −14%, holds 1,758 providers, over a third of the population. The second, at −30% to −26%, holds 994, about a fifth. Between them sit three modest bins (310, 256, 724). To the right of the main peak the counts collapse to 71, 82, and 80 in the three bins nearest zero. To the left of the second peak the tail runs 290, 150, 123, and finally 15. Two providers actually gain.

We carved out four clusters for discussion. They are not exhaustive; roughly 1,800 providers fall in the connective tissue between them.

Group 1, Winners (N=162): −6% to +2%. Essentially untouched.

Group 2, Average (N=1,758): −18% to −14%. Close to the headline CMS number.

Group 3, Losers (N=994): −30% to −26%. Nearly double the headline.

Group 4, Big Losers (N=138): −46% to −38%. Since the statutory 15%-per-year phase-in compounds to only −38.6% after three years, this group is defined by cuts that take four or more years to fully unwind.

The dollars are as lopsided as the counts, in the opposite direction. Group 1 holds $1.1 billion in baseline, 19% of the total, but absorbs $6 million of the cut, under 1% of the pain. Group 2 holds $2.4 billion and absorbs $398 million, almost exactly proportional. Group 3 holds $720 million but absorbs $200 million, 22% of the pain on 12% of the dollars. Group 4 is a rounding error in aggregate, $21 million, though not to the practices inside it.

Group 1: Commercial and Medicare Already Agreed

The 162 providers nearest zero split cleanly by bin.

The 80 in the −2% to +2% bin are the proprietary molecular diagnostics industry. Several sit at exactly 0.00%.

The adjacent 82 at −6% to −2% have a different character: 34 clinical labs, but also 29 ophthalmology and optometry practices, which is every eye-care provider in the eligible population, nearly all at a modeled change of −2.00% to two decimal places. That is not twenty-nine negotiations converging. That is one code with a −2% adjustment and a specialty that bills little else on the CLFS besides 83861. 

This bin also holds academic reference labs (Mayo, Washington University, Johns Hopkins) and specialty molecular firms (Exagen, Biodesix, Interpace, Assurex) that are lightly exposed to a commoditized code or two, so they fall short of 0% change.

Two providers gain outright: Caris at +44% and Pacific Edge at +33%, both proprietary molecular tests. It's rare but when the private median exceeds the Medicare rate, PAMA raises the rate, and the increase carries no phase-in cap.

Group 2: Mainstream Medicine Plus the Duopoly

The main peak is the clinical mainstream. Internal medicine (474) and family practice (247) make up 41% of the group; hematology-oncology and medical oncology add 275; endocrinology, rheumatology, nephrology, and cardiology add 272. These are practices running routine chemistry, hematology, and endocrine panels.

The 354 clinical labs are where the money is. Labcorp's North Carolina NPI ($208 million, −17.7%) and New Jersey NPI ($146 million, −16.0%), Quest's New Jersey ($113 million, −16.5%) and Florida ($106 million, −16.9%) entities, Unilab, Sonora Quest, and a parade of other Labcorp and Quest state entities all land within a point or two of −16.5%. The national labs are, by construction, the "average" cut, and I argue below that this is a mechanism rather than a coincidence.

What is absent is as telling as what is present. Zero pain management or anesthesiology providers. Only 28 nurse practitioners and 19 urologists out of 1,758.

Group 3: A Specialty Roster - It's Not a Random Draw

The second peak looks nothing like the first. Nurse practitioners (197) and physician assistants (90) make up 29%. Urology contributes 131, most of the eligible urologists. Pain management, interventional pain, and anesthesiology contribute 192. Podiatry contributes 38 of the 41 podiatrists in the study.

The 241 clinical labs are a distinctive cohort too: HealthTrackRx ($49 million, −27.9%), CAP Diagnostics, Sierra Genomics, Genetic Technological Innovations, Patients Choice of Indiana, and a long list of Texas, California, Florida, and Arizona LLCs named Lone Star Tox Screen, RealTox, QualiTox, DrugScan, Equaltox, Dominion. Many bunch at −29% to −30%, the group's left edge. A pathologist recognizes the toxicology and PCR-panel sector without being told.

One corporate detail deserves a pause. A Labcorp NPI in North Carolina sits in Group 3 at −29.2% on $9.6 million, while the Labcorp flagship sits in Group 2 at −17.7% and a small Labcorp NPI in New York sits in Group 1 at −5%. Quest has an Oregon NPI in Group 1 at −6%. BioReference has a Texas NPI at 0.0% and its New Jersey flagship at −19.7%. Same company, same contracting department, same leverage, three groups. Whatever is sorting providers, it is not bargaining power.

Group 4: Small Dollars, One-Code Portfolios

The 138 providers losing 38% or more total only $21 million in baseline, but they lose roughly 40 cents on the dollar. Nurse practitioners (41), physician assistants (22), pain and interventional pain (32), anesthesiology (16), and PM&R (9) make up 87% of the group, mostly individual practitioners with $50,000 to $150,000 baselines in Texas, Oklahoma, Michigan, Florida, and New Jersey.

The exceptions are a dozen clinical labs, two of them surprising: Invitae ($753,000 under this NPI, −42.9%) and Baylor Miraca Genetics ($709,000, −39.9%) are germline genetics labs, not tox shops.  Potential suspects: 81412 Ashkenazy panel fell from $2448 to $1591, and 81443   pan ethnic carrier panel fell from the same $2448 to even lower new rate, $1461.  

The 1,800 in Between

The unclustered providers include the biggest single losers in dollars: Aegis ($33 million, −36%) and Millennium ($21 million, −35%), just outside the Big Loser cutoff. Myriad ($34 million, −25%), Silverpath, and Genview sit just right of the Loser peak. Prince Laboratories ($61 million, −9.7%) and Reliable Results ($33 million, −9%) sit between the Average peak and the Winners. This is where the large, diversified, multi-code operators live; their position is a weighted blend of the four archetypes.

What Is Going On: A Microeconomist's Reading

PAMA sets each code's price at the volume-weighted median of private-payer payments for that code. Specialty never enters the formula. The specialty sorting is therefore a second-order effect: specialties run characteristic code portfolios, and the codes face characteristically different commercial markets. The question is why those markets differ so much.

The Winners: When You Are the Only Seller, the Median Is Your List Price

For a sole-source proprietary test, the "private-payer median" is not the output of a market. It is the output of one lab's contracting book. If the lab simply does not below its Medicare rate, and that rate was itself set by crosswalk, gapfill, or ADLT pricing at a level the lab proposed, the median comes back at exactly the Medicare rate and the change is exactly 0.0%. The Medicare rate holds for 2026; the same rate except paid by Medicare Advantage commercial plans rather than the MAC, pegs the rate for 2027.

The eye-care cluster at −2.00% is the same story in a different key: a single dominant code, 83861, few suppliers, a commercial price that barely moved.  Ophthalmologists receive $7M in Medicare lab payments, of which $6.2M is 88361, microfluidic tear osmolarity analysis used to diagnose and evaluate dry eye disease.  It's about $22 and fell 2%. It has no CMS controls (no articles or LCDs). Two providers in California, both optometrists, reaped $273,113 and $256,540 with 8 tests per patient and about 1500 patients each.  (Small chart at bottom.  Subset data here $7M, all national providers at bottom, $10M).

The Average Group: The Duopoly's Contracts Become Everyone's Price

For routine chemistry, hematology, and endocrine codes, the volume-weighted median is dominated by Labcorp and Quest because they bill the overwhelming majority of commercial volume. Their discounted contract rates are the median, and the median becomes the Medicare rate. The national labs land at the average cut almost tautologically. In a sense they wrote it.

The 1,200 physician practices in Group 2 are price-takers in a negotiation they never attended. An internist with an in-office analyzer did not discount CPT 80053 to Aetna; Quest did. But the internist inherits Quest's discount through Medicare. This externality is the heart of PAMA's design. Diversified across dozens of commodity codes, these practices get the blended commodity result, about −16%.

The Losers: A Prisoner's Dilemma in Which Medicare Was the Prize

Toxicology, PCR infectious-disease panels, and the urine, wound, and nail-fungus molecular codes that dominate pain, urology, podiatry, and advanced-practice billing face a fragmented commercial market with hundreds of small labs competing for the same specimens. Payers have managed these codes aggressively for a decade through lab benefit managers, prior authorization, and narrow networks.

The structure is a classic prisoner's dilemma. Each lab, chasing network access or referral flow, discounts. Each discount is individually rational. Collectively, the discounting drags the median down 28% to 30%, and PAMA then imports that median into Medicare, which for many of these labs was the best-paying customer they had. The commercial price war destroyed the Medicare price. Nobody intended it; nobody could have prevented it alone.

A selection effect compounds this. PAMA counts only claims actually paid by private payers. An out-of-network tox lab that bills $800, gets denied, and writes it off contributes nothing. The in-network labs that accepted $120 contribute everything. The median is a median of those who capitulated.

The practitioners in Group 3 are riding the same codes. A pain practice billing definitive drug testing under its own NPI, or a urology group running PCR panels in-house, has a one- or two-code CLFS portfolio. Their result is simply the code's result.

The Big Losers: Concentration Risk

Group 4 is Group 3 without diversification. An NP with $80,000 in Medicare lab revenue has it in one or two codes, and if those were the codes where the commercial market collapsed most completely, the practice gets the full cliff.

The genetics labs are the puzzle. Invitae and Baylor Miraca at −40% or worse does not fit the tox story. Either these NPIs carry a narrow set of panel or cytogenetic codes that high-volume competitors undercut dramatically, or the corporate flagships sit elsewhere and these are satellites with unrepresentative portfolios. Either way, the lesson matches the pain practices: in a per-code median system, a concentrated portfolio is an undiversified bet on one commercial market.

The Timing Game

We do know:  Because cuts are capped at 15% per year, the difference between a −28% lab and a −43% lab does not appear in payment until 2029 or 2030. That is three to four years in which every actor in the losing groups has the incentive and the time to respond: migrate toward codes with smaller adjustments, restructure which NPI bills what, shift volume to the physician fee schedule where possible, exit the worst codes, consolidate, or lobby for another delay. Goodhart's Law [when a measure is treated like 'the target] applies in full. The fully-phased histogram projects what happens if everyone holds still, and in the losing groups nobody will.

Conclusion

The fully-phased PAMA distribution is not a bell curve with a mean of −16%. It is four economic regimes on one axis. The proprietary molecular sector controls its own median and sits at zero. Mainstream medicine inherits the national labs' discounts and sits at the headline average. The toxicology and PCR-panel sector, with the specialties that bill those codes, competed its commercial prices down and now watches Medicare follow, near −28%. A thin tail of one-code portfolios absorbs 40% or more.

The most useful fact in the data may be the corporate one: the same company occupies three groups under three NPIs. PAMA does not sort providers by size, leverage, or sophistication. It sorts them by code, and the codes sort by the structure of the commercial market each lives in. 

That is a very different policy from the one Congress thought it wrote in 2014, when the premise was that Medicare overpaid for lab tests across the board. What it built instead is a transmission line that imports each commercial sub-market's competitive structure, be that monopoly and price war alike, into the Medicare fee schedule, one code at a time.

[claude fable, using output #2]

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83861.  $22, PAMA change -2%.  All providers 2024, 1715 rows (NPIs).  Services 491,298 for 181,113 patients, 2.7 per patient.  5 providers paid >$100,000 (up to   $  263,113).   The top 2 were optometrists in Maryville and Yuba City CA (7-8 tests per patient, for about 1500 patients.) 
The following charts PAMA raw data for price reports.  



Puzzling PAMA Pricing Data #1. Profiles of Six More Genetic Tests.

 In several blogs we've gone past the median price discounts in various lab services.  We've actually profiled the range of  reported PAMA raw price information - the shape of the marketplace.

We showed that some codes, which have a right-sided isolated peak of high prices, can have as much as half of profit coming from those sky-high outliers. Here.

Here, we do price profiles for six additional genetic tests.  These are:

  • 81162, BRCA 1-2
  • 81432, BRCA Panel
  • 81241, Factor V (one of the most common genes)
  • 81412, Ashkenazi Carrier Panel
  • 81419, Epilepsy panel, a recent boom-code in FL, TX
  • 81443, Pan-ethnic carrier panel
Profiles are based on actual observed PAMA medians, that is, what would be fully-phased in prices.
Figures, click to enlarge.
I don't know why the profiles vary so much, but it's like snowflakes, no two alike.







Monday, November 2, 2026

AMA CPT Now Releases Editorial Panel "Meeting Minutes"


Header: For AMA CPT Meetings, AMA will now released full minutes (100-pages-plus.)

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The AMA has two major reimbursement-related committees: the CPT Editorial Panel and the Relative Value Update Committee- the RUC.

For years, detailed meeting minutes of the RUC have been online.  In contrast, AMA CPT posted only an abbreviated "Summary of Panel Actions" - e.g. code applications that were voted in, rejected, or postponed.

Now, AMA CPT posts both the "Panel Actions" and also detailed "Minutes" (over 100 pages) for CPT meetings.

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The updated web page for editorial actions says, "To provide transparency in the decision-making process, meeting minutes and voting results, along with the summary documents, are included on this page for the last five years."  However, as of now, I only see meeting minutes for September 2026 (Minneapolis CPT).

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Find the CPT page here:

https://www.ama-assn.org/about/cpt-editorial-panel/summary-cpt-editorial-panel-actions-meeting-minutes

Find the RUC page here:

https://www.ama-assn.org/about/rvs-update-committee-ruc/ruc-recommendations-minutes-voting

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Friday, October 30, 2026

Very Brief Blog: Pennsyvania Passes Colorectal BIomarker Coverage State Law

 Here's a mini post to guide you to a press release at Guardant.  

Pennsylvania has passed a biomarker law, HB 1123, aligning CRC coverage with ACS aand NCCN guidelines.

https://investors.guardanthealth.com/press-releases/press-releases/2026/Guardant-Health-Applauds-New-Law-Ensuring-Access-to-Colorectal-Cancer-Screening-Innovations-with-No-Cost-Sharing-for-Pennsylvanians/default.aspx


Signed by governor October 22; see more at state Sen. Devlin Robinson.  

See the home page for HB 1123.  The house floor vote was 187 to 16. 

Look for a gold star banner which I think is the final version of PN4076.  Fiscal notes show no fiscal impact on the state (because it affects private insurance not state budget insurance.)



When Websites Go Down; McDermott Plus

In an adjacent blog, I asked Claude to find publication dates for PFS and OPPS rules.  It sourced the data from different sites.  Sometimes it used McDermott plus, where you might find snippets of archives but the main site went down when the entity closed around September 1.

I noted my own Discoveries in Health Policy has had 8M views and 3500 articles in over ten years.

Here's what Claude commented:

Confirmed: Law360/MLex reports that McDermott Will & Schulte is closing its healthcare lobbying and consulting shop after a dozen years. That's a real loss for this corner of the field. 

The McDermott+ rule summaries were the fastest, most reliable day-of digests of PFS and OPPS releases, and their OPPS/PFS data dashboard was the only free tool I know of that let you track APC and RVU payment rates across years without rebuilding it from the addenda yourself. Their "Healthcare Preview for the Week" was also one of the few places that tracked the regulatory calendar prospectively rather than just reporting what had already dropped.

The pattern is worth naming. Law-firm and consultancy thought capital lives on a corporate website whose existence depends on the business unit surviving a partnership vote, and the content has no archival home of its own. When the unit closes, the content isn't moved to a successor page; it's just unplugged. The same thing happened to Health Policy Alternatives' public material, to much of the Avalere grey literature, and to a fair bit of what the Advisory Board published before the Optum acquisition. The Wayback Machine catches some of it, but WordPress ?p= URLs and PDF attachments are exactly what it captures least reliably.

When Will CMS PRS and OPPS Final Rules Appear? Today? Next Week? CLAUDE knows.

 Annual "final rules" for Physician Fee Schedule and Outpatient Prospective Payment System (PFS and HOPPS or OPPS) come out around November 1, always after "market close."  November 1, so they are 60 days ahead of the implementation date of January 1, 2027.

I asked Claude for a look-back and here's what I got.  BTW the query took about a minute.  The rules are named for the upcoming calendar year. Today on October 30, 2026, we're awaiting the rule for CY27.

Short answer: November 1 and November 2 each appear about half the time.  Usually, both rules on same day.    

And: Claude, 1 minute, Chat GPT 4 minues.  All answers matched.


#### CLAUDE

Innovation Always Lives in an Ecosystem (featuring Guardant)

In a recent analysis, Guardant has nearly $1B in market cap per 100 employees, rising to $25B market cap and 2500 employees since its founding fourteen years ago.  Here's an essay built on that theme.

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Innovation Must Live in an Ecosystem

Sometimes the most interesting number is one you weren’t looking for. Yesterday I was reading an article tracing Illumina’s financials over 15 years. A few hours later, a simple question popped into my head: how much stock market value does a diagnostics company have for every 100 employees? I put together a comparison using published market caps and employee counts. I expected differences, but I surprised by the valuation range, when using this metric.

The Milken essay by AmirAli Talasaz, Guardant


Chat GPT 6

On October 26, 2026, the Milken Institute published AmirAli Talasaz’s “Proactive Health Care Takes Innovation and Responsive Policymaking” in its Power of Ideas series. The essay argues that prevention and early detection deserve greater prominence in health care, using colorectal cancer screening as its principal example. [1]

Market Cap per 100 Employees: Guardant and Natera the Clear Leaders

We have the multiple of share price over revenue, and share price over netcome.  Another one is share price per 100 employees.   For this metric, many molecular labs rank high, but Guardant and Natera lead the pack at about $1B market cap for every 100 employees hired.

Here's the chart:



click to enlarge

Quest has market cap $28B, 57,000 employees, per 100, $49M.  
LabCorp has a market cap of $30B, 71,000 employees, per 100, $42M.

And here's a graphic.  Natera and Guardant are both on the far right of the xy chart, with market cap around $1B per 100 employees.   Natera is twice as high as Guardant, since its market cap is $65B vs $25B.

click to enlarge