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 50 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 8p PDF WHITE PAPER... HERE.

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. 
• 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

[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. 

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.

CMS PULLS ALGORITHMIC LAB SERVICES OFF THE CLFS POLICY SYSTEM: Final Rules CY2027

Find the inspection copy OPPS rule here:

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

Find the inspection copy PFS rule here:

https://www.federalregister.gov/public-inspection/2026-22743/medicare-and-medicaid-programs-calendar-year-2027-payment-policies-under-the-physician-fee-schedule

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AI CORNER

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EXECUTIVE SUMMARY

• CMS has finalized a major change in Medicare payment policy for algorithm-only laboratory tests, effective January 2027. 
• CPT codes previously paid under the Clinical Laboratory Fee Schedule (CLFS) (n=14) will instead be treated as other diagnostic tests.  That means, they'll be paid under rules for the Hospital Outpatient Prospective Payment System (OPPS) or the Physician Fee Schedule (PFS). 
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• Initial payment rates will approximate existing CLFS rates, but conventional Medicare coinsurance will apply.  
• Laboratory-specific rules, including the 14-day date-of-service rule, will no longer apply. 
• CMS corrected several code classifications following public comments but retained its fundamental policy. 
• Important questions remain about future pricing, packaging, and regulation.

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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.

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.

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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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.

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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

All About the Precision Medicine World Conference - Santa Clara - Jan 27-29, 2027

 

For the Precision Medicine World Conference - 3 days at the end of January 2027 -here's the website:

https://pmwcintl.com/

And here's the agenda:

https://pmwcintl.com/program/

I'm on a panel this year.  The panel is chaired by Gabriel Bien-Willner, and includes Dawn Cardeiro of Roche Diagnostics, Stacey Brown of Cepheid, and Asad Ali Ahmad of Network Bio. Friday, 2 pm, track 3, session 3.  From Signal to Standard of Care: What makes a non-cancer blood test actionable?

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PMWC 2027: A Detailed Guide to the Conference and Its Agenda

 

The Precision Medicine World Conference returns to Santa Clara January 27–29, 2027, bringing together researchers, clinicians, diagnostics developers, biotechnology companies, investors, and policy specialists. Its agenda spans artificial intelligence, cancer detection, advanced therapeutics, genomics, and emerging applications beyond oncology. For prospective attendees, the central attraction is the opportunity to examine how promising technologies become useful, accessible, and financially sustainable care. Precision Medicine World Conference, Jan. 27-29

What kind of conference is PMWC?

The Precision Medicine World Conference, usually called PMWC, is a three-day meeting about the science, technology, and business of tailoring healthcare to individual patients. Formerly known as the Personalized Medicine World Conference, it began in 2009 under founder and president Tal Behar, working with Stanford Health Care. The 2027 Silicon Valley meeting is scheduled for January 27–29 at the Santa Clara Convention Center, with Stanford, UCSF, and Yale identified as co-hosts. PMWC LLC organizes the event. pmwcintl.com

The conference brings several professional communities into the same setting. Academic investigators describe discoveries; biotechnology and diagnostics companies discuss development and commercialization; clinicians and health-system leaders consider adoption; investors evaluate opportunities; and policy specialists examine access and payment. That mixture gives the meeting particular relevance to people whose work crosses institutional boundaries. A laboratory developing a new assay, for example, may need conversations about clinical evidence, physician use, reimbursement, and financing as much as it needs feedback on the assay itself. pmwcintl.com

The organizers advertise more than 3,000 attendees, 400 speakers, and 150 sponsors for 2027. These are promotional figures rather than independently verified attendance totals. The announced speaker and honoree roster includes Emmanuelle Charpentier, David Baker, Mary-Claire King, Eric Lander, Fei-Fei Li, Andrew Ng, Eric Horvitz, and Daphne Koller. The breadth of that roster illustrates the conference’s combination of molecular medicine and computation. Precision Medicine World Conference, Jan. 27-29

The format includes five main tracks, two company showcase tracks, exhibits, award presentations, and networking receptions. The main tracks contain scientific, clinical, and industry discussions; showcases emphasize company technologies and projects. PMWC also offers a startup competition, renamed in 2026 to honor Atul Butte. Its commercial orientation is explicit and useful for interpreting the meeting: attendees will encounter research presentations alongside product positioning and business development. PMWC Precision Medicine World Conference

For a first-time visitor, the main practical challenge is selection. Multiple simultaneous sessions mean that even an attendee staying all three days will see only part of the program. Prospective participants would benefit from identifying a few central questions and arranging relevant meetings before arrival. A particularly strong use of PMWC is to connect a scientific development with the clinical and commercial decisions that determine whether it reaches patients.

The 2027 agenda

The advertised 15-part program consists of five main tracks, each with a different theme on each of the three days. The company showcases are additional. The following descriptions summarize the posted program as reviewed on October 29, 2026; individual sessions and speakers remain subject to change. pmwcintl.com

1. Advanced therapeutics

Track 1 progresses from engineered cell and gene therapies on Wednesday to immunotherapies and targeted treatments on Thursday, then genetic and RNA medicines on Friday. The program covers competing cell-therapy approaches, manufacturing, delivery into the body, treatment resistance, patient selection, genome editing, and RNA splicing. Listed participants across this area include Katy Rezvani, David Miklos, Antoni Ribas, Emmanuelle Charpentier, Adrian Krainer, and Randy Schekman. LinkedIn

The notable feature is the attention to development constraints. Manufacturing and delivery can determine whether a persuasive biological idea becomes a practical treatment. Sessions addressing individualized therapies also raise a consequential question: how much evidence and infrastructure can be shared across treatments that differ from one patient to another? For biotechnology executives and translational researchers, these discussions may offer more actionable information than a broad survey of new therapeutic mechanisms.

2. Artificial intelligence and computational medicine

Track 2 follows AI through three different settings: diagnosis on Wednesday, clinical deployment on Thursday, and drug discovery on Friday. Diagnostic topics include computational pathology, AI-supported companion diagnostics, genomic interpretation, and combinations of different data types. Thursday includes clinical agents, foundation models, real-world evidence, and AI governance. Friday features computational protein design and drug-development discussions, with David Baker and Daphne Koller among the advertised participants. PMWC Precision Medicine World Conference

This structure helps separate questions that are often bundled together under the label medical AI. A system that interprets a pathology image, one that supports clinical work, and one that proposes a drug target face different tests of usefulness. The clinical program’s inclusion of evaluation and governance suggests that deployment experience will be an important counterweight to demonstrations. Drug-discovery attendees can look for evidence about experimental validation and development success, beyond improvements in computational prediction.

3. Precision diagnostics

Track 3 offers an especially coherent three-day sequence for laboratory and diagnostics audiences. Wednesday addresses early detection and intervention, including risk-based screening, precancerous disease, and the integration of molecular and imaging signals. Its opening discussion reassesses the accomplishments and shortcomings of the first generation of early-detection approaches. The listed contributors include Victor Velculescu, Rebecca Fitzgerald, Nickolas Papadopoulos, and Phil Castle. PMWC Precision Medicine World Conference

Thursday turns to liquid biopsy for treatment selection and monitoring. Topics include whether molecular residual disease, or MRD, should guide additional treatment; how blood tests can identify response and resistance; how tissue, blood, and imaging information should be reconciled; and whether MRD can support accelerated drug approval. Max Diehn and Sarah-Jane Dawson are among the featured investigators. The drug-endpoint session lists Ash Alizadeh as chair and participants including Ola Landgren, Chris Hourigan, and Aparna Parikh. PMWC Precision Medicine World Conference

Friday expands beyond cancer to transplant monitoring, infectious-disease testing, and maternal and fetal health. It also addresses the clinical actionability and reimbursement of non-cancer blood tests. The posted agenda lists Bruce Quinn among the participants in that discussion, chaired by Gabriel Bien-Willner. PMWC Precision Medicine World Conference

The diagnostic track’s strongest analytical thread is the connection between a test result and a subsequent decision. Early detection raises questions about whom to test and what follows a positive result. Monitoring raises questions about when treatment should change. A drug-trial endpoint raises another question: what does a biomarker establish about treatment benefit? These distinctions make the track relevant to clinical-development and market-access teams as well as assay developers.

4. Genomic medicine and implementation

Track 4 begins with genomic diagnosis and broader measurement of human health, including unresolved genetic disease, population and newborn screening, and structural variants. Thursday focuses on implementing precision medicine across healthcare organizations. Friday examines aging, prevention, and healthspan, including metabolic interventions, biological measurements, and digital monitoring. Eventbrite

The Thursday implementation program deserves particular attention from readers concerned with adoption and reimbursement. A session titled “What Gets Covered and Why” lists Dan Knecht, Mike Montalto, Tamara Syrek Jensen, and Shane Mull. Other discussions address operating models, clinical decision support, and the transition from biomarker evidence to routine care. These topics recognize that clinical adoption requires coordinated changes in payment, information systems, and professional practice. PMWC Precision Medicine World Conference

The aging program warrants a different evaluative lens. Attendees can look for a clear account of what a proposed measure changes in clinical care, how an intervention will be evaluated, and which outcomes matter to patients. The inclusion of aging alongside established genomic medicine creates opportunities for comparison, but the maturity of the evidence will differ across individual applications.

5. Spatial biology and disease applications

The new fifth track covers spatial biology on Wednesday, autoimmune and inflammatory disease on Thursday, and neurology on Friday. Spatial biology studies cells and molecular signals in their tissue context. The program connects that approach with digital pathology and biomarker development, then considers immune-directed therapies and biomarkers across autoimmune disease. pmwcintl.com

Friday’s neurology program includes Alzheimer’s blood biomarkers, other neurodegenerative disorders, imaging, digital measurements, and barriers to broader clinical adoption. Henrik Zetterberg, David Holtzman, Li-Huei Tsai, and David Liebeskind are among the leaders highlighted in the organizer’s neurology materials. The stated emphasis on treatment selection and access makes this more than a survey of biomarker discovery. pmwcintl.com

The broader significance of this track is the extension of precision medicine into diseases where patient classification, treatment selection, and longitudinal monitoring are developing along different paths from oncology. Attendees interested in translating an approach from one specialty to another can examine which assumptions survive the move and which require new evidence.

What the agenda suggests about the field

Taken together, the program suggests a field increasingly occupied with the consequences of its own technical progress. More sensitive assays, richer biological data, and more capable AI systems expand what can be measured or predicted. The conference repeatedly returns to the harder question of what healthcare should do with that information. This is an interpretation of the agenda, rather than a claim that the scheduled discussions will resolve those problems.

Three connections are especially useful for prospective attendees.

First, diagnostics and therapeutics are becoming harder to discuss separately. A screening test needs a credible follow-up pathway; an MRD result needs a defined decision; a targeted therapy needs a workable patient-selection strategy. An attendee who crosses between the diagnostic and therapeutic tracks may gain more than one who stays within a familiar technology category.

Second, evidence is becoming a shared concern across AI, laboratory medicine, and health-system implementation. The relevant questions include whether a finding generalizes, whether it changes practice, and whether the resulting change benefits patients. Reimbursement discussions add a practical perspective: what evidence would justify paying for that use? The agenda offers opportunities to compare those standards across specialties, while leaving room to question whether a company’s preferred evidence answers the purchaser’s question.

Third, the expansion into aging, immunology, and neurology makes careful comparison of development stages essential. A scientific mechanism, a validated measurement, a clinically useful test, and an adopted service represent different achievements. The presence of all four at one conference can be informative, provided attendees keep those distinctions in view.

Who is likely to benefit?

PMWC appears particularly well suited to diagnostics developers, biotechnology leaders, translational researchers, investors, and professionals working on clinical implementation or market access. Its breadth may also benefit clinicians seeking a view of technologies moving toward their specialties. Attendees seeking extended instruction in one technical method, or a detailed course in Medicare coding and payment mechanics, should inspect individual sessions closely before deciding whether the program meets that narrower need.

The meeting is in person, with no livestream announced. A full conference pass covers all three days, the main and showcase tracks, exhibits, and receptions. Eligible recordings and slides are offered afterward, subject to speaker permission. The organizer does not offer separate one-day or exhibit-only passes and advertises reduced rates for eligible academic, government, nonprofit, and student attendees. Current prices and participation details are available through the registration page. PMWC Precision Medicine World Conference

For readers weighing the trip, the most useful test is whether the agenda brings together the people and questions needed for a current project. A short list of relevant sessions, several planned conversations, and time reserved to explore an adjacent field would give attendance a clear purpose.

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Compare the agendas for 2024 and 2027..

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Comparing 2024 and 2027 at the PMWC

Comparing PMWC’s 2024 and 2027 agendas reveals continuity, but a sharper emphasis on clinical decisions, implementation, and expanding precision medicine beyond its established domains.

The foundation remains recognizable: cell and gene therapies, oncology, liquid biopsy, genomic analysis, artificial intelligence, and commercial showcases. Neither AI nor implementation suddenly arrived in 2027. The 2024 program already included foundation models, computational pathology, deployment barriers, reimbursement, and real-world evidence. The change is one of specificity and organization.

AI shows the clearest evolution in vocabulary and ambition. In 2024, many sessions emphasized data sharing, harmonization, privacy, and exploring AI’s potential. By 2027, clinical agents, multimodal diagnostics, enterprise deployment, and governance occupy prominent positions. Drug discovery increasingly asks whether computational predictions survive experimental validation and improve development outcomes. The discussion has moved toward evaluating systems that institutions might actually purchase and operate.

Diagnostics similarly becomes more explicitly decision-oriented. The 2024 liquid-biopsy program surveyed clinical status, prevention, and multicancer detection. The 2027 agenda asks whether MRD should trigger additional treatment, when resistance should prompt a therapeutic switch, and how conflicting tissue, blood, and imaging results should be reconciled. MRD as an endpoint for accelerated drug approval receives explicit attention. Screening remains important, but its consequences and limitations receive more pointed examination.

Several subjects lose standalone prominence. Pharmacogenomics occupied an entire day in 2024, with substantial attention to standardization, education, ordering, and reimbursement; it persists in 2027 without comparable billing. Women’s health and biological sex differences also lose their dedicated day, although maternal and fetal diagnostics remain. Infectious disease and microbiome topics are redistributed into broader diagnostic and immunology programs. These are changes in conference emphasis, not evidence that the fields have stalled.

The largest expansion is a fifth main track covering spatial biology, precision immunology, and neurology. Aging and healthspan also gain a dedicated day. Therapeutics places greater emphasis on programmable medicines, delivery, and manufacturing. 

Collectively, the agenda suggests growing interest in connecting biological measurements to interventions and workable care pathways. For returning attendees, the strongest reason to return is the opportunity to examine those connections across specialties, rather than simply revisit familiar technologies with newer examples.