WISeR AI Program: What Happens When CMS Gives No Clear Instructions But Authorizes "AI"

 The WISeR program for AI-mediated autodenials is coming into effect now.  For a couple dozen procedures, physicians/hospitals will have to submit pre authorization paperwork to special CMS subcontractors, which will have several days to greenlight or deny the surgery. (Submit to, Zyter, Virtix, Humata, Cohere, Genzeon, Innovaccer.)  

CMS has stated that the contractors will simply be submitted existing CMS rules and instructions (LCDs, NCDs).   But this won't always work.  For example, penile implant codes are among the controlled codes under WISeR:  including 54400, 54401,  54405.

CMS  says the companies will follow CMS coverage rules under NCD 230.4.

Really?   NCD 230.4 says nothing:

• Impotence is a failure of a body part for which the diagnosis, and frequently the treatment, require medical expertise. 
• Depending on the cause of the condition, treatment may be surgical; e.g., implantation of a penile prosthesis, or nonsurgical; e.g., medical or psychotherapeutic treatment.

So I don't know what rules preauthorization will use, but it's literally impossible for surgeons, hospitals, or patients to know in advance whether a case is "in" or "out" of payment rules that will be applied behind the scenes.  Because CMS gives no concrete rules.

(Note also, the NCD is not just about surgery, but covers any kind of medical or psychological intervention).

An online article discusses the vendors.

CMS Gears Up for Auto-Denials of Nerve Stimulation Code 64568 (WISeR)

 On December 31, 2025, CMS issued Change Request 14205 (aka Transmittal 13570).  The title is,

Implementation of Wasteful and Inappropriate Service Reduction (WISeR) Model Prior Authorization and Medical Review Process and Establishment of New Quarterly Change Request (CR) Process for Possible Future Changes.

Find it here:

https://www.cms.gov/files/document/r13570demo.pdf

The 82-page document covers many implementation aspects for WISeR, the program that will allow AI-facilitated autodenials for a range of clinical interventions.

What caught my attention first was Table 7 (page 28), which applies from January 2026 to December 2031 in a number of large states, including New Jersey, Ohio, Oklahoma, Texas, Arizona, and Washington.  (The list of states can be expanded at the discretion of the Center for Innovation, CMMI).   

The instructions focus on one CPT code, 64568, which is cranial nerve stimulation.  See also table 8 (page 30), and Attachment B, Table 1, which states that edits designed around NCD 160.18 will be deployed.  This NCD covers vagal nerve stimulation for partial seizures therapy and for treatment-resistant depression under particular clinical protocols.  (The CPT code is further described in Attachment C, 64568, page 53.)   

However, as you'll see below, the same CPT codes may be used for quite different procedures that are variably inside or outside of WISeR, suggesting room for errors, at least early on.

The instructions include Attachment F, associated codes under WISeR, which links 64568 to NCD 160.18 again, but also links 64568 to L38307, L38310 and L38385, where 64568 is cited in connection to LCDs for hypoglossal nerve stimulation.   (Code 64582 is also linked to the same three LCDs).

Page 81 is a model letter showing the relevance to providers.  If the provider practices in AZ, NJ, oH, OK, TX, or WA, and will supply at least one of the nominated medical servics, the provider has two options to demonstrate medical necessity.  One is to submit a prior authorization request for every case.  Or, they can perform the service without prior authorization, and the claim will undergo pre-payment medical review at the MAC.  The MAC has 45 days to review the un-authorized claim.

The document has 16 pages of detailed step-by-step instructions, but it is unclear to me how the prior auth companies or the MACs will handle CPT codes that serve multiple purposes (some covered by WiSER, others not.)

Attachment A: WISeR Model Program Files (page 23)

Attachment B:  NCDs, LCDs, included in the WISeR Model (page 49)

Attachment C: Codes Requiring Prior Authorization (page 50)

Attachment D:  WISeR Model Participants (page 54)
(Zyter, Virtix, Humata, Cohere, Genzeon, Innovaccer.)
(An online article discusses the venders.)

Attachment E: Claims Data Elements (page 55) 

Attachment F: Associated Codes (page 56)

Attachment F is described as, "the codes associated with/related to services require PA, but which will not have prior authorization requests submitted for them directly."

64568 appears in five places, on page 28 Table 7 (under Attachment A, Program Files), page 30 Table 8, page 53 (under Attachment C, requiring prior authorization),  page 60 (associated codes), page 80 (associated codes).  

_______________

In the current CPT, code 64568 is for cranial nerve stimulation, "e.g." vagus, and 64582 is for hypoglossal nerve stimulation (for sleep apnea, with certain code requirements).   64582 also appears on page 41, 42,  50, and 60.

Some of these codes have diverse uses.  For example, in the 2025 Billing Guide for Inspire V (hypoglossal nerve stimulation), 64568 is listed as, may be the initial implant code:

The Inspire IV billing guide gives 64582 as, may be the initial implant code.

CMS coding and pricing changes can lead to volatility (here, here).  The overlapping notation of various codes in different ways in different tables in WISeR suggests that the WISeR protocols will need to be implemented carefully.

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One might say the solution is, the WISeR process will stop any claim with an epilepsy or depression code (re the vagus NCD) but pass any claim with a sleep apnea code.  But many elderly men could have both medical conditions.  

It's crytpic why a WISeR edit that is stated as directed to vagus nerve stimulation (a particular NCD) should also include any edits at all for hypoglossal nerve stimulation (64582) which has nothing to do with the vagus nerve NCD for epilepsy and depression (64568).

Finding Drug Labels - DAILYMED @ NLM NIH

I usually find drug labels via Google, which may flag the FDA website, or a manufacturer website, or other sources.

The CMS OPPS drug passthrough application referred to drug labeling at a website run by National Library of Medicine, DAILY MED.  New to me.  Here it is:

https://dailymed.nlm.nih.gov/dailymed/index.cfm

_____________

Update: Two colleagues pointed out there is ALSO a similar, and more historical, database at FDA;

https://www.accessdata.fda.gov/scripts/cder/daf/

____________

TESTING DAILYMED...

Want Herceptin?  They got it.

https://dailymed.nlm.nih.gov/dailymed/drugInfo.cfm?setid=492dbdb2-077e-4064-bff3-372d6af0a7a2

Want Gozellix, a new prostate cancer PET scan biomarker?  They got it.

https://dailymed.nlm.nih.gov/dailymed/drugInfo.cfm?setid=abeab169-e38e-433a-b8a9-f1dab31518c8

##

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And here's the FDA website, which has a long comprehensive arcive of updates to Herceptin's 1998 labeling:

https://www.accessdata.fda.gov/scripts/cder/daf/index.cfm?event=overview.process&ApplNo=103792

Are We Dumping Good Innovations into a Chaotic "Ecosystem?"

See an essay at Linked In by the prolific Alex Bendersky on whether it's realistic to expect good results when we dump fine innovatios into a chaotic "ecosystem" of healthcare stakeholders.

Essay here.

His take-off point is a new article by Reed, Svedberg, Nygren, in J Med Internet Res, called:

Enhancing the Innovation Ecosystem: Overcoming Challenges to Introducing Information-Driven Technologies in Health Care.

Find the article open access here.

Rouby et al: Can Community Pharmacogenetics Be Cost-Saving?

Much has been written about pharmacogenetics for 20, maybe 30 years, but basic issues like PGx panel coverage by payors have remained uncertain.

This week the public Pharmacogenomics group at Linked In - which has almost 3000 members - features an article by Rouby et al on the value of community-focused PGx.   For my money, the main issue is that it is not an RCT and there can be a lot of issues with assigning "controls" or "comparables" based on things like same age or same number of chronic diseases or other propensity scoes.

• PGx Linked In group here.
• Essay about Rouby et al. here.
• Open access article by Rouby et al. here.
• It runs in CTS, Clinical & Translational Science, part of ASCP.

The authors' abstract is here:

FDA Opens Comment on Rubrum Petition to Exempt Much AI from Review

 In October 2025, Rubrum Advising,  in service of Harrison AI, submitted a 34-page petition to the FDA asking for widespread exemptions from  review (from premarket notification) of certain medical AI software.   Find it here.

In Federal Register December 29, FDA requests public comment on the petition; comment open for 60 days.   Find it here.

At Linked In, J David Giese discusses, here.

Below, Chat GPT weighs in.

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Sidebar:  New article by David Horgan et al. on medical AI in Europe - here.

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HUMOR: The World's Most Detailed Apple AIRTAG Instructions

It's the holidays, we're traveling, we're using Apple Airtags to track luggage and backpacks.  Every two years you change the battery, which takes about 10 seconds and one sentence of instructions.

I asked Chat GPT to make the most comically detailed description possible.  Humor only.  Below.

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Find it at my sidebar blog, here:

https://bqwebpage.blogspot.com/2025/10/very-very-very-very-detailed-airtag.html

PopEVE - An Important New Advance in AI-Driven Clinical Genomics

 Header - PopEVE is a new AI model that is being called "transformative" for clinical genetics.

##

• See news story at OncoDaily here.
• See the primary publication by Orenbuch et al. in Nature Genetics (Nov 24) here.
• See an interesting earlier publication for backgroud, AlAbdi et al., Nat Comm, 2023, here.

###

I have some concerns that this type of major advance in clinical genomics may collide at high speed and head-on with changes the AMA plans to "Appendix S" (AI) in the AMA CPT, and to a possible future "software registry" (to be called CMAA) placed far outside the normal coding and payment system we use in the clinical laboratory profession and industry.    This topic is Agenda 64 at the February AMA CPT Palm Springs meeting (see entry point here.)

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AI Corner

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Here's a ChatGPT condensed version of the original abstract:

Missense variants pose ongoing challenges for genetic interpretation due to context-dependent effects and poor calibration across the proteome. We developed popEVE, a deep generative model integrating evolutionary and human population data to estimate variant deleteriousness at a proteome-wide scale. popEVE achieves state-of-the-art performance without inflating deleterious variant burden and identifies variants in 442 genes in a severe developmental disorder cohort, including 123 novel candidates.
Notably, popEVE can prioritize likely causal variants using child-only exomes, enabling diagnosis without parental sequencing. This work demonstrates a generalizable, evolution-informed framework for rare disease variant interpretation, particularly for singleton cases.

### 

Review: PopEVE — A Proteome-Wide, Calibrated Model for Missense Variant Interpretation

1. Context: why missense interpretation remains hard

Missense variants sit at the most difficult intersection of molecular biology and clinical genetics. Unlike loss-of-function variants, their effects are graded, context-dependent, and protein-specific, making them resistant to binary pathogenic/benign classification. Large Mendelian sequencing studies have shown that diagnostic failure is often not due to sequencing gaps but to interpretive bottlenecks, including novel allelic effects, misleading in-silico predictions, and variants whose significance cannot be reliably inferred within a single gene context (Nature Communications).

PopEVE directly targets this bottleneck: not by adding another pathogenicity classifier, but by reframing variant interpretation as a proteome-wide calibration problem (Nature Genetics).

CMS Issues Final Lab Fee Schedule for January 2026 (CLFS)

On December 23, 2025, CMS released the final fee lab fee schedule (aka CLFS) for January 2026.

https://www.cms.gov/medicare/payment/fee-schedules/clinical-laboratory-fee-schedule-clfs/files

See file, "26CLABQ1" CY 2026 Q1.

There was just one notable change (Natera Signatera rate, $3920 falls to $3590).   

Note, however, that many older lab tests (often routine clinical chemistry) were scheduled to fall up to 15% on January 1, delayed by legislation to January 31.   Without further legislation, some conventional CLFS rates will fall in February and the rest of 2026.

Signatera

Signatera entered ADLT pricing at $3500, and rose as high as $3920 for CY2025.   ADLT rates are based on the ADLT lab's commercial payor pricing, in this case, for 1HCY2024 submitted data to CMS in 1Q2025 effective as median price starting1Q2026.  That is, Natera (I believe) would have known its January 2026 CLFS rate for its ADLT as soon as claims for 1H2024 were on the books.

Puneet Souda of Leerink noted, the pending new ADLT price "has been widely telegraphed and expected for nearly a year."

Experience Shows...

Often, or usually, ADLT prices are quite stable year to year (not even changing a penny).  I believe the simplest interpretation is that Medicare Advantage plans are at least 51% of commercial volume and, such Medicare Advantage plans are paying the lab at the effective CLFS rate.   

"AI, Please Find Me 10 Important Genomics Headlines from the Past Month"

Header:  What do you get if you ask AI for "Ten Important Genomics Headlines from the Past Month?"

While I was grocery shopping, it occurred to me to ask my AI (Chat GPT 5.2) to find ten interesting and important clinical genomics headlines from the past month.    I had no idea if it could do that or not.

In fact, it gave me two ten-item lists to choose from (its List #1 and its List #2).   When I asked, it also told me how List 1 differed from List 2 (different search instructions).

So below, you'll see LIST NUMBER ONE, then LIST NUMBER TWO, then AI explains how it came up with the two different styles.

Note: The original AI report sometimes had multiple links per citation.  When copied to my blog, only the first link is maintained.  The symbol +2 or +3 indicates that the original native report had had more links.  

See also original output here;

https://chatgpt.com/share/694ac73b-c4b4-8005-9eef-743dd34d2ad2

Two Fascinating AI Tasks: (1) Windows Software Repair; (2) A New Viewpoint on Point of Care Testing

 I had two unusually interesting experiences with AI at Work this week.  In one, Chat GPT diagnosed a quite arcane computer software problem, involving a commerical site, my lap top, and my router.

In the other, I asked Chat GPT to take a kind of weird unexpected position on the value of Point of Care Testing, and it came back with some interesting ideas.

### SOFTWARE DEBUGGING

Header: Chat GPT fixed a complicated multi-part software problem that sidelined my work for half a day.

Nowadays, we rely heavily on cloud-based software—Google Drive, for example. I use a database and information-management system called Notion. For a day or two, it began performing terribly: web pages refused to open or took up to a minute to load. Even after simple pages finally appeared, a progress indicator kept spinning, as if the system were still trying to download something.

My first assumption was a (rare) server-side problem at Notion. But their status page showed everything was up and running. ChatGPT suggested a simple test: run my desktop computer using my iPhone’s Wi-Fi hotspot instead of my home network. When I did, Notion worked dramatically better.

At that point, ChatGPT concluded that the problem wasn’t Notion, and it wasn’t my computer—it was my router.

After some back-and-forth troubleshooting, we disabled the 6 GHz band on my router (which supports 4/5/6 GHz). ChatGPT explained that this also disables experimental router software associated with Wi-Fi 7. Once we made that change, Notion immediately returned to normal performance.

The solution sounds straightforward when explained this way, but the path to it was anything but obvious. I never would have guessed that a hard-wired Ethernet desktop could be disrupted by a Wi-Fi setting—especially one (6 GHz) I hadn’t even known existed.

(Boring, but long dialog here.)

###  NEW IDEAS IN POCT

Header:  When asked to provide an unusual frame and outlook for the value of point of care testing, I thought Chat CPT came up with an interesting and clever approach.

##

I was reading several recent review articles on point-of-care testing (POCT) when a stray thought crossed my mind: Shannon information theory. I have only a superficial understanding of the theory, but out of curiosity I gave ChatGPT a set of current POCT review articles and asked whether it could identify any new or interesting value implications by viewing POCT through that theoretical lens.

What came back was, at minimum, genuinely interesting. ChatGPT reframed POCT not simply as a logistical or workflow innovation, but as an information-processing system, and explored how concepts from information theory might help explain observed advantages of POCT in clinical practice.

To my eye, this was a good example of AI doing something more than summarization or pattern matching—it was generating novel perspectives and structured ideas in response to an open-ended question.

Important caveat: I am not claiming that this constitutes publishable, ground-breaking research, or that it should form the basis of a PhD thesis. Rather, it struck me as a thoughtful and somewhat surprising example of AI producing genuinely interesting conceptual work when prompted in the right way.

For more detail of what it actually said, here.

Summary of its novel POCT ideas here:

Conventional health economic and outcomes research (HEOR) analyses of point-of-care testing (POCT) emphasize turnaround time, workflow efficiency, and operational convenience. In the present project, a paired conceptual analysis applies Shannon information theory and modern decision-theoretic frameworks to re-examine POCT as an information-processing system rather than a logistics innovation. 

Using qualitative reasoning and simplified mathematical formulations, the essays model the diagnostic encounter as a communication channel linking latent disease states to clinical action. The analysis demonstrates that POCT fundamentally restructures this channel by reducing information loss (“erasures”), preserving clinical context, enabling feedback-driven sequential decision-making, and aligning diagnostic information with disease dynamics. Even when analytic sensitivity and specificity are identical to central laboratory testing, POCT increases effective information yield, improves physician–patient communication fidelity, and raises the expected utility of diagnostic information. At the system level, same-visit clarification reduces care-pathway entropy, improves triage accuracy, and stabilizes downstream resource allocation. 

This new information-theoretic framing provides a unifying explanation for observed POCT advantages reported in the [conventional] HEOR and implementation literature and offers a complementary theoretical foundation for evaluating POCT value beyond speed, cost, or assay performance alone.

Brief Blog: CMS Unleashes Major Drug Pricing Proposals

 If you want to bury something, CMS says release it on Friday night.   If you really want to bury it, use the Friday night before Christmas week.

See open access coverage at Axios.

Proposals peg US drug prices to prices (benchmarks) in other countries.  There are two separate rules.  One, GlOBAL BENCHMARK or "GLOBE," is Part B injectibles.  The other, GUARD, is Part D drugs.

See GLOBE press release here.  Innovation website here.   Fed Reg here.  The proposal is 279 pages; see paginated version Dec. 23.

See GUARD press release here.  Innovation website here.  Fed Reg here.   The proposal is 281 pages long.

Here's the TLDR note:

The Trump administration proposed two Medicare drug pricing pilots tying U.S. payments to international benchmarks. A Part B model (2026–2031) and a Part D model (2027–2031) could save Medicare about $26 billion total and reduce seniors’ outpatient out-of-pocket costs by $6.2 billion. However, prescription drug costs for seniors could rise $3.6 billion longer term as manufacturers offset losses by raising other prices. Both models exclude drugs already under Medicare negotiation. Pharma groups strongly oppose the plans, warning of higher costs and reduced R&D.

My Point Would Be: Is CMMI Unconstitutional? 

I've blogged since 2010, the ACA created CMMI and gave it authority to rewrite any aspect of Medicare law, for the purpose of a demo model.  But, the demo models have no definition or constraint in space or time or scope.   The concern is basically this: under the Constitution, Congress is supposed to make the law, not hand over a blank check to an agency to rewrite it with no limits. When Congress gives an executive agency the power to waive any part of Medicare law, for any kind of “demonstration,” with no limits on what counts as a demo or how long it can last, that starts to look like Congress giving away its own law-making authority. The Supreme Court has often said that delegations must come with clear boundaries and guidance; an open-ended, unlimited delegation risks violating the separation of powers.  In legalese, "Nondelegation doctrine: Congress may not delegate its legislative power to the executive without providing an “intelligible principle” to guide and limit the agency’s discretion."

Brief Blog: CMS Updates Price Transparency

Header: CMS is arguing for important updates to its Price Transparency rules.

See:

• News at Fierce Healthcare here.
• CMS press release here.
• CMS FAQ here.
• CMS proposed rule here.
• See paginated Fed Reg version Dec 23.
• The work follows Executive Order 14221, “Making America Healthy Again by Empowering Patients with Clear, Accurate, and Actionable Healthcare Pricing Information.” (2-25-2025)

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AI CORNER

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I asked ChatGPT to review the documents and also, to let us know if there was any interesting emphases or deletions in the press release, relative to the proposed rule.   Comment 60 days (about Feb 21).

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Below is a blog-ready review of the Transparency in Coverage Proposed Rule (CMS-9882-P), written with your policy-savvy readership in mind. I focus first on what the actual proposed rule does, then compare it with the press-release framing, and finally flag notable provisions that are largely absent or understated in the press messaging.

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CMS Proposes Major Reset of Payer Price Transparency Rules (CMS-9882-P): What Actually Changes

On December 19, 2025, CMS, jointly with DOL and Treasury, released a sweeping proposed update to the 2020 Transparency in Coverage (TiC) rules. While publicly framed as a “burden-reducing” cleanup, the proposed rule is more accurately described as a structural redesign of how payer price data must be generated, organized, contextualized, and consumed .

The core policy diagnosis is blunt: TiC succeeded in forcing disclosure, but failed in usability. CMS identifies three systemic problems—file size, ambiguity, and misalignment with hospital price transparency—and the proposal is aimed squarely at fixing those defects.

1. From “Plan-Level” to “Network-Level” In-Network Rate Files

Perhaps the most consequential change is the requirement that In-Network Rate Files be reported at the provider-network level, not separately for each plan or product. CMS recognizes that dozens (or hundreds) of plans often share identical networks and rates, and current plan-level reporting is massively duplicative.

Why it matters:
This is a fundamental data-model shift, not a formatting tweak. It aligns payer disclosures with hospital price transparency conventions and implicitly treats provider networks—not plans—as the economic unit of price formation.

2. Mandatory Exclusion of “Unlikely Provider–Service” Combinations

Plans would be required to strip out negotiated rates for services a provider would never realistically perform, using the plan’s internal provider taxonomy already used for claims adjudication.

CMS goes further and requires public disclosure of:

• The taxonomy mapping file itself

• A Utilization File listing providers who actually billed for services over a defined lookback period

• A Change-Log File showing what changed quarter-to-quarter

Why it matters:
This quietly moves TiC from a “dump everything” regime to a curated, defensible dataset—and exposes plans’ internal logic for determining who is paid for what.

3. Substantially More Out-of-Network (OON) Data

CMS explicitly acknowledges that Allowed Amount Files have been nearly useless due to the 20-claim threshold and short lookback window. The proposal would:

• Aggregate OON data by insurance market type (large group, small group, individual, self-insured)

• Lower the threshold from 20 to 11 claims

• Extend the reporting window from 90 days to 6 months, with a 9-month lookback

Why it matters:
This is not cosmetic. CMS expects materially more OON pricing data to enter the public domain, enabling analysis of balance-billing exposure and market-type pricing behavior.

4. Quarterly (Not Monthly) Reporting

CMS proposes to move In-Network and Allowed Amount files from monthly to quarterly updates, while keeping prescription drug files monthly.

Why it matters:
This is framed as burden reduction, but it also implicitly acknowledges that rates are sticky, and that analytic value does not increase with frenetic refresh cycles.

5. New Contextual Data Elements

Each file would newly include:

• Product type (HMO, PPO, etc.)

• Enrollment counts

• Common network names

This is CMS conceding that raw prices without context are analytically misleading.

6. “Findability” Requirements

Plans must post:

• A plain-text (.txt) file in the website root identifying file locations and contacts

• A standardized “Price Transparency” footer link on the homepage

This mirrors hospital transparency enforcement and is clearly designed to support automated crawling and third-party analytics.

7. Single File Format Under Consideration

CMS explicitly floats the idea of mandating a single file format (JSON or CSV) via future technical guidance.

Why it matters:
This is a signal that CMS sees standardization—not flexibility—as the next phase of transparency policy.

8. Phone-Based Price Transparency (No Surprises Act Alignment)

Plans would be required to provide the same price-comparison information by phone as online or in print, and CMS explicitly states that satisfying this requirement also satisfies No Surprises Act §114.

Why it matters:
This quietly expands operational obligations and training costs—and pulls grandfathered plans into certain disclosure duties.

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How the Press Release Frames It (and What It Downplays)

The CMS press release emphasizes:

• Smaller files

• Easier consumer use

• Empowerment and competition

• Burden reduction

• Alignment with Trump-era transparency goals

All accurate—but selectively so .

What’s Missing or Underplayed in the Press Messaging

Here are important features in the rule that are largely absent or muted in the press release:

1. Disclosure of Internal Provider Taxonomy Logic
   The rule requires plans to expose how they internally decide which providers can be paid for which services. 

• This is new transparency into payer decision-making, not just prices.

1. Utilization Files as a De-Facto Validation Layer
   The Utilization File requirement effectively creates a ground-truth layer against negotiated rates—something the press release barely mentions, despite its analytical importance.

2. Magnitude of New Compliance Costs
   The regulatory impact analysis estimates over $900 million in one-time compliance costs, driven largely by Utilization Files and Change-Logs—numbers absent from public messaging .

3. Shift Toward Machine-First, Not Consumer-First, Transparency
   Despite consumer-friendly rhetoric, the rule is optimized for developers, researchers, employers, and enforcement, not casual patient shopping.

• May serve as input for AI, other advanced data analyses.

1. Quiet Expansion of Federal Authority via No Surprises Act Crosswalk
   CMS explicitly states that satisfying TiC phone-based disclosure requirements satisfies NSA obligations—even for some grandfathered plans. This is a regulatory consolidation move not highlighted publicly.

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Bottom Line for Policy Watchers

This proposed rule is not just a cleanup. It represents CMS’s tacit admission that first-generation transparency failed at usability, and that the second generation will be more structured, more enforceable, and more analytable—even if that means exposing payer logic and imposing nontrivial new compliance costs.

For data users, this is a clear win.
For plans and TPAs, it is a quiet but significant operational escalation.
For CMS, it signals a pivot from “disclosure as ideology” to disclosure as infrastructure.

Comment deadline: February 21, 2026.

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I work in clinical genomics, how do I summarize in 30 seconds for my boss?

####

Two bullets for your lab industry boss (executive-level)

• CMS is hardening price-transparency data into infrastructure, not optics.
  The TiC rewrite forces payers to publish cleaner, contextualized, machine-usable datasets (networks, utilization, enrollment), signaling that CMS increasingly expects pricing data to support analytics, benchmarking, and enforcement—not just consumer browsing.

• This strengthens payer leverage and normalization pressure—eventually affecting advanced diagnostics pricing.
  Better out-of-network and network-level data improves payer ability to benchmark negotiated rates and utilization patterns, which over time increases pressure toward price rationalization even in complex service categories.

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Why this matters (if your boss asks “so what?”)

1. It’s upstream of molecular pricing, not adjacent to it
The rule doesn’t touch labs directly—but it improves payer visibility into how prices behave across markets and networks. Once that infrastructure exists, it becomes easier for CMS and commercial payers to ask uncomfortable questions later about:

• why certain services sit outside norms,

• why OON utilization persists,

• why network participation differs by market type.

That matters for molecular labs because complexity has historically protected pricing. CMS is slowly dismantling opacity as a structural defense.

2. It aligns with CMS’s broader “data-first” regulatory arc
This rule fits the same pattern you see in:

• Hospital Price Transparency enforcement tightening

• No Surprises Act operationalization

• CMS interest in AI/analytics using administrative data

For diagnostics companies, the takeaway isn’t “price transparency is coming for us tomorrow,” but rather:

CMS is building the data rails first; policy follows later.

3. It subtly favors scale and sophistication
Smaller plans and TPAs will struggle more with utilization files, taxonomy logic, and change logs. Large payers and integrated systems will adapt faster—and those are the same actors that increasingly shape lab network inclusion and reimbursement terms.

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What I would not overstate

• ❌ No immediate impact on CPT pricing, PLA rates, or MolDx

• ❌ No new disclosure requirements for labs

• ❌ No short-term revenue effect

This is strategic weather, not tactical risk.

• Draft comment-letter themes for payers, employers, or analytics firms

• Compare this rule to hospital price transparency enforcement trajectories

• Analyze implications for AI-driven price comparison tools and data licensing

AMA Posts Agenda for Palm Springs AMA CPT Meeting (Feb 5-7, 2026)

Header: AMA has posted the complete agenda for the February 5-7, 2026, AMA CPT editorial meeting in Palm Springs.

Note that pathology/laboratory codes were posted on an earlier comment timeline, which is now closed.  (You can still get the code info, but it won't get to the lab subcommitees). For all other topics, the deadline for comments is January 6.  

To comment, you have to apply for AMA permission to comment at least a few days ahead of January 6.  See more at the first page of the agenda pdf;

https://www.ama-assn.org/system/files/cpt-panel-february-2026-agenda.pdf

CPT AI POLICY IN FLUX: "APPENDIX S" (tab 68)

AMA is working on making changes to its AI policies all across the CPT.  One feature is updates to the "Appendix S" about AI.  

By December 19, AMA had already posted "option A" and "option B."  Appendix S is Agenda Item 68 on page 11 of the agenda PDF.

Finding Parts of Tab 68.  In the CPT app that you use to become a commenter, note that there are four columns in the app for the row "Tab 68," Appendix S.  The first column symbol givs you a word document that explains the committee's rationale for Appendix S revisions.   The second column has a symbol you tap to submit a structured box-by-box comment portal.   The fourth column is an extra document which is Ballots A and B.  This are the revision for a new Appendix S, as "A,' and, a "B" version which is a minor edit of "A."

ROLE OF APPENDIX S

I mentioned that Item 68 givs you a word document that explains the commitee's rationale.  This is the standard CPT code change document with many questions.    

In the agenda for Feb 5-6, I see references to AI about 11times (informal count).

In the CPT application template (I used the Appendix S version), I see references to AI or to Appendix S twice - at III.2 and at V (AI Specific section). 

"We Don't Define AI, Except Where We Define AI"

While Appendix S says point blank AMA does not define AI, Code Change Section V identifies software as AI or not-AI.  In CPT Applicaiton, V, "Artificial Intelligence," software is identified and defined (or classified) as AI by the AMA CPT "if software has performed more than data processing."  (!)  Data processing is then defined in a parenthetical:  "(data processing includes helping to aggreggate, organize/arrange, transmit, develop, or otherwise visually enhance the data.)"

FINAL WORD ON APPENDIX S

It's hard to evaluate Appendix S without knowing its purpose.  Yes, in part it defines "assistive, augmentive, autonomous" AI (except it doesn't define AI!).  But why?   And later in the new Appendix S, autonomous AI is broken into three parts.  There's not the faintest clue why, so it's hard to know if that table is fit for purpose or not, having no purpose.

Better would be:  I would prefer they include a page about why they have Appendix S, how Appendix S is "used" in practice at CPT, and maybe 10 problems with the current system, 10 points where Appendix S addresses a problem, and how Appendix S fixes the problem.  This kind of structured end-oriented thinking is wholly absent from Appendix S and its change documents.  

AI CODES

While there are nearly a dozen AI codes, nearly all in Category III, one is "AI algorithm generated synthetic nerve potentials" 0278T, which the applicant hopes to elevate to Category I ("97XX5.")  Note that this is the applicants hope; the CPT may or may not buy the upgrade.  

LAB TESTS

Of particular interest, the PLA committee has been declining to take AI-augmented lab tests, at lesat in digital pathology.  Should those land in Category III  instead??  Watch in real time: In Tab 44, an applicant asks for 2 codes for "algorithmic image analysis on cancer tissue," I think on H&E sections.  They asks for two codes - I'm guessing, perhaps one could be starting with a slide to image, and one could be starting with a DICOM.  Proposals for X548T X549T.

Also with a nod to the PLA committee, which seems to now exclude WSI on IHC because IHC often includes physician work, there is an applicant for Synucleinopathy detection on immunofluorescent slides.   Tab 46 for X547T.   Stained skin slides predict Parkinson's disease presence or absence.   

The above are of interest because there have been very, very few Cat III codes for lab tests up until now.  Apparently Cat III is accepting refugees from PLA.

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MECHANICS & DATES

Note that if you comment, you are sent to a "CPT Smart App Portal" where you have to enter the part of the portal for "Interested Party" in a top-of-page field.

dates:

More Big News: HHS Request for Information - Artificial Intelligence Reimbursement!

Header:  HHS Issues Important New RFI on AI Reimbursement - Connections to Digital Pathology.

It's been a big several weeks for news related to digital pathology and healthcare AI more generally.  I'll quick link to several very recent blogs, then we jump into today's big news, an AI RFI from HHS that includes Reimbursement explicitly.

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My Blogs:

• Is Digital Pathology Outrunning the AMA CPT Process?  December 13, 2025.
• CMS Current Approaches to AI Reimbursement (MedPAC 2024) December 16, 2025.
• HHS RFI - AI REIMBURSEMENT.   This blog today.

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• For an entry point to the HHS RFI, see a Linked In posting on December 19 from Steven Posnack, HHS.
• Posnack is the Principal Deputy Assistant Secretary for Technology Policy at the HHS Assistant Secretary for Technology Policy/Office of the National Coordinator for Health IT.  He holds a Master's in health policy from Hopkins and has been in government IT health policy since 2018.
• Also see Steven Posnack's standalone article on December 19 at healthItBuzz here.
• Read the HHS Press Release on December 19 here.
• And see the RFI itself here - 7 pages in pre-publication format. 
• 60 day comment period - around February 15.
• See Fed Reg Dec 23. 
• News also from HealthCare Dive here.

Excited to see a whole section under the title, "REIMBURSEMENT."

Reimbursement 

HHS’s payment policies and programs have massive effects on how health care is delivered in the United States, often times with unintended consequences. Hypothetically, if a payer is taking financial risk for the long-term health and health costs of an individual, that payer will have an inherent incentive to promote access to the highest-value interventions for patients. 

Under government designed and dictated fee-for-service regimes, however, coverage and reimbursement decisions are slow. Rarely does covering new innovations reduce net spending; and waste, fraud, and abuse is difficult to prevent, often times leading to massive spending bubbles on concentrated items or services that are not commensurate with the value of such products. 

Given the inherent flaws in legacy payment systems, we seek to ensure that the potential promises of AI innovations are not diminished through inertia and instead such payment systems are modernized to meet the needs of a changing healthcare system. We seek feedback on payment policy changes that ensure payers have the incentive and ability to promote access to high-value AI clinical interventions, foster competition among clinical care AI tool builders, and accelerate access to and affordability of AI tools for clinical care.