Friday, December 20, 2024

United Healthcare and Palmetto DEX: Scope of Codes Reaches Phase 4

See a December 18, 2024, update at Palmetto DEX for the latest expansion-of-scope in which United Healthcare commercial plans require Palmetto DEX  Z-codes for claim processing.

Find the article here:

https://www.dexzcodes.com/palmetto/dex.nsf/DIDC/XHHX62A8AE

It links to a PDF file of CPT (or PLA) codes that require co-listed Z codes for processing:

https://www.dexzcodes.com/palmetto/providers.nsf/files/Commercial_Reimbursement_CPT_Codes.pdf/$FILE/Commercial_Reimbursement_CPT_Codes.pdf

Stating in part, The policy will require the submission of a Z-Code obtained from the DEX® Diagnostics Exchange Registry for claims to be considered for reimbursement. Claims for molecular pathology services will be denied if the Z-Code information is missing...



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The PDF document above is divided into 4 tables, each matching to a Phase.

According to the article, Phase I including Medicare-relevant tests, prenatal carrier tests, and two kinds of 81479 tests (PGx and reproductive management carrier screening).   Phase 2 included thromboembolism risk tests and high-risk inherited cancer panels.  Phase 3 added exome/genome sequencing and "prenatal cell free DNA screening."  Finally, now, Phase 4 includes "all remaining molecular pathology relevant CPT codes [Phase 4 code list].

By my count, Phase 1 had 326 codes, Phase 2 had 111 codes, Phase 3 had 30 codes, and Phase 4 had 192 codes, totalling 569 CPT codes, assuming no duplicates amongst the 4 tables.

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For the Palmetto DEX webpage on its commercial payer programs (last updated 11/2023) see:

https://www.dexzcodes.com/palmetto/dex.nsf/DID/DEJUF0KKWB


MolDxOlogy Meets DomainOlogy - Multiple Routes to MolDx Tech Assessments

By using Google, I stumbled on two separate websites for MolDx tech assessments.   They look similar, so at a glance, it is almost as if one is "phishing" or "spoofing" the other.  But they're not the same.

MEDICARE MOLDX

The Medicare MolDx website is here:

https://www.palmettogba.com/moldx

Note that the core domain is "palmettogba.com."   

It looks like this:


For example, in a green box in the middle, you click on Tech Assessment, and it takes you to a second webpage with several links provided (here).  One of the options is Tech Assessment Forms, last updated 7-17-2024.  Click on that, and you get to this webpage with about 15 forms.

https://www.palmettogba.com/palmetto/moldxv2.nsf/DIDC/TJ4XC2M5IX~Technical%20Assessment


Now, let's  start over and go back to the Palmetto.com MolDx home page.

This has a box stating, "Important Update" and saying to get a Z code, go to the Z code registry.  OK, that's been the case for several yearsa.  But there is also a fine print remark that says,

If you are a provider not billing for Medicare services within the MolDX Jurisdictions, go to the DEX® Diagnostics Exchange website for information.

Click on that, and you go here:

https://palmettogba.com/palmetto/dex.nsf

Note that this is still palmettogba.com.


If you click HERE on tech assessments, you come to this page:

https://palmettogba.com/palmetto/dex.nsf/DIDC/UMPUJNFSV0~Technical%20Assessment

But it's not the same!  

First, it has about five  extra forms (such as Genome and such as Prenatal Carrier Screening.)  

And, the top form, the Checklist, has the same name as we saw before (GEN CQD 003) but it is a different version (v3) and has a different update date (now updated 10/17/2024).   So Palmetto is creating the same forms with same code names but different versions on different websites.

Further, while I just screen-shot the DEX TA commercial forms as  on a palmettogba.com link (above), there is also the same page under the domain of DEXZCODES.COM:

https://www.dexzcodes.com/palmetto/dex.nsf/DID/UMPUJNFSV0



##

There are other links between Palmetto Medicare MolDx.   For example,  the top link in this blog (the Palmetto Medicare Moldx link) has a notice, dated 5/2023, that CMS billing and coding articles with lists of tests and services, "are in the process of being removed" and "coverage criteria can readily be found in the DEX registry."   However, one difference, which might be important in some contexts, is that the CMS coding articles are archived by change date and list updates, and are public access, while the DEX require email registration to access behind a firewall and materials are copyrighted.  In contrast, for Medicare Advantage plans, CMS requires MA coverage criteria to be open access and specifically not require a personal tracking email on each entrance.

click to enlarge





Thursday, December 19, 2024

Journal Club: "Contextualizing the Future of AI in Pathology" (Singh et al.)

Archives of Pathology and Laboratory Medicine is running a series of articles on AI in pathology.  Here's the most current one:  Introduction to Generative Artificial Intelligence: Contextualizing the Future, by Singh et al, released December 5, 2024.  Open access. 

The article is one-stop-shopping for a very impressive and broad overview of AI, and future articles will delve further into a series of pathology applications.

https://meridian.allenpress.com/aplm/article/doi/10.5858/arpa.2024-0221-RA/504263/Introduction-to-Generative-Artificial-Intelligence

Don't miss the supplementary file here.    See Eric Glassy's Linked-In post here.





AI Corner

Chat GPT 4o reads and summarizes Singh et al.

Summary for Professional Readers: Generative AI in Pathology and Beyond

This paper provides a comprehensive framework for understanding Generative Artificial Intelligence (GAI) and its applications in pathology, while addressing the broader implications for health care. It is tailored for a professional audience of pathologists, laboratory scientists, and stakeholders in medical innovation.

Key Contributions and Insights

  1. Positioning GAI in Pathology: The article contextualizes GAI as a transformative tool with applications spanning diagnostic support, workflow optimization, and research. GAI tools, such as OpenAI’s GPT series and image-based platforms like Google’s Vision Transformers (ViTs), are positioned to augment the accuracy and efficiency of diagnostics.

  2. Framework for GAI Evaluation: The authors propose a structured approach to evaluating GAI models, emphasizing human-in-the-loop methodologies to mitigate risks such as hallucinations (fabricated or inaccurate outputs) and biases inherited from training data. This dual emphasis on creativity and reliability is essential in critical clinical contexts.

  3. Clinical Implementation Challenges: A detailed examination of validation and verification processes reveals the inadequacy of current frameworks to handle the dynamic nature of GAI. With no FDA-approved GAI-based devices to date, the paper underscores the need for novel evaluation metrics, such as perplexity for language models and enriched datasets for medical diversity.

  4. Ethics and Risk Mitigation: The paper explores ethical concerns, such as data privacy, algorithmic bias, and transparency. It emphasizes the importance of retrieval-augmented generation and enhanced explainability for trustworthiness in clinical environments.

  5. Real-World Applications: Specific applications in pathology include:

    • Automated annotation of pathology slides to improve diagnostic precision.
    • Simplified patient-facing pathology reports in native languages.
    • Accelerated drug discovery through novel molecule generation.
  6. Regulatory and Risk Management: The discussion incorporates frameworks such as ISO/IEC 42001 and NIST AI Risk Management Guidelines, advocating for robust governance, interdisciplinary collaboration, and the development of institution-specific safeguards.

Distinctive Contributions

This paper moves beyond common discussions on AI to provide actionable recommendations for integrating GAI into pathology. Its emphasis on practical applications, evaluation frameworks, and interdisciplinary collaboration positions it as a pivotal resource for advancing GAI adoption responsibly in medical fields.

The article serves as both an introduction and a call to action, encouraging pathologists to engage actively with GAI development and regulation to ensure ethical and effective integration.


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SIDEBAR 1
###

10 Key Takeaways for Advancing Generative AI (GAI) in Medicine

  1. Pathologists as AI Stewards:
    GAI positions pathologists not merely as end-users but as active stewards and collaborators in AI development, ensuring alignment with clinical needs and patient outcomes.

  2. Retrieval-Augmented Generation:
    One solution to hallucination risks is retrieval-augmented generation, where GAI systems pull verified information from external knowledge bases, improving trust and factual accuracy in outputs.

  3. Transformative Multimodal Systems:
    Multimodal GAI models (e.g., combining text, images, and audio) can revolutionize digital pathology workflows, such as integrating microscopic findings with structured clinical notes or generating image-driven summaries.

  4. Focus on Local Validation:
    Validation frameworks must account for local variability in pathology datasets, such as diverse tissue types, staining artifacts, or regional disease prevalence, ensuring clinical applicability.

  5. From Reactive to Proactive Governance:
    ISO/IEC 42001 and NIST Risk Management Frameworks advocate proactive governance, emphasizing the need for incident reporting, ethical training, and structured public feedback loops in GAI oversight.

  6. Expanded Definition of Bias Mitigation:
    Beyond demographic fairness, GAI must address domain-specific biases, such as overlooking pediatric pathology due to sparse data in certain subpopulations.

  7. Emphasis on Explainability:
    Pathologists should champion explainable AI models that provide confidence scores, reasoning paths, and uncertainty metrics, especially for nuanced diagnostic decisions.

  8. Synthetic Data for Rare Cases:
    Leveraging synthetic datasets generated by GAI for underrepresented conditions can bolster model robustness and expand its utility in rare disease diagnostics.

  9. Risk of Automation Bias:
    Overreliance on GAI’s outputs risks automation bias, underscoring the importance of human-in-the-loop systems where expert judgment remains integral to patient care.

  10. Collaboration as a Mandate:
    Effective deployment of GAI in medicine requires cross-disciplinary collaboration among pathologists, data scientists, ethicists, and policymakers to address technical and societal challenges holistically.

This framework highlights the transformative potential of GAI when implemented thoughtfully, while maintaining a commitment to ethical, evidence-based practices in healthcare.

###
SIDEBAR 2
###

Three Discussion Questions for a Journal Club on Generative AI in Medicine

  1. Ethical Challenges and Governance:
    Given the inherent risks of hallucinations and bias in GAI models, what innovative strategies or safeguards can pathologists advocate for to ensure ethical deployment without stifling innovation? How might these strategies differ for high-risk clinical applications versus administrative or educational tasks?

  2. Interdisciplinary Roles in GAI Development:
    The paper emphasizes collaboration between pathologists, data scientists, and other stakeholders. What specific roles should pathologists play in the development and fine-tuning of GAI models, and how can they effectively bridge the gap between clinical needs and technical capabilities?

  3. Future of Validation Standards:
    Current FDA and regulatory frameworks are limited in addressing the dynamic and probabilistic nature of GAI outputs. How can validation and evaluation standards evolve to better assess the reliability and safety of GAI in clinical pathology? Should synthetic data and real-world testing environments play a larger role?

###

Prompts.

Please summarize this paper for professional readers of my blog, which is on innovation in medicine, especially in genomics and pathology in general.   The readers are medical school faculty, PhD level company employees, etc.    Look for things that this article is really contributing to the framework and discussion and not simply commonplaces or truisms. 

#

On the theme, " Its emphasis on practical applications, evaluation frameworks, and interdisciplinary collaboration positions it as a pivotal resource for advancing GAI adoption responsibly in medical fields"...    Please  give a sidebar with ten key takeaways, again, focusing on what is original or interesting, and not simply a commonplace like "Regulations should be appropriate."  

#

For a journal club, provide three interesting discussion questions.

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Reviewed in the style of Lewis Black.




Friends of Cancer Research: Digital Pathology and More, February 4, 2025

Friends of Cancer Research has a half-day conference (hybrid, with streaming), on Tuesday, February 4, 2025.  Register here:

https://friendsofcancerresearch.org/event/advancing-the-future-of-diagnostics-and-regulatory-innovations/

In person, it's 10 am - 2 pm ET, at DC Ritz Carlton. I've also cut-pasted the agenda at bottom of this blog.



##

See their recent publication on validity of whole slide imaging for AI-Her2 interpretations:

https://www.discoveriesinhealthpolicy.com/2024/12/friends-of-cancer-research-whole-slide.html

##

Friends of Cancer Research is hosting a meeting focused on driving innovation in diagnostics and regulatory policy. 

Key discussions will highlight evidence generation for novel technologies for diagnostic testing, including digital pathology and artificial intelligence (AI), along with strategies for developing diagnostic tests for rare biomarkers and addressing future regulatory considerations for cutting-edge tools. 

New data will be released from the ongoing Digital PATH Project, which leveraged a common dataset to evaluate variability across digital pathology platforms. This meeting brings together key thought leaders to explore how these innovations can accelerate progress in precision medicine and improve patient outcomes.


10:00AM: Welcoming Remarks

10:05AM: Morning Keynote / Michelle Tarver, Director, CDRH, U.S. FDA

10:30AM: Panel 1 Discussion: Evaluating Digital Pathology and AI in Diagnostics

11:45AM: Panel 2 Discussion: Validating Diagnostic Tests for Rare Biomarkers

12:45PM: Lunch

1:15PM: Panel 3 Discussion: Advancing Regulatory Frameworks and Policies for AI in Healthcare

1:55PM: Closing Remarks

###

SIDEBAR:

You might enjoy Glass et al, Deployment of a Machine Learning Algorithm in a Real-World Cohort for Quality Control Monitoring of Human Epidermal Growth Factor-2–Stained Clinical Specimens in Breast Cancer, Arch Path Lab Med here.   

  • Concluding, Automated image analysis for HER2 scoring is consistent and reliable using this algorithm. Deployment of the HER2 quality control tool across 3 clinical laboratories revealed interlaboratory variability in HER2 scoring and inconsistencies in data reporting.  
  • These results support the future incorporation of quality control algorithms for real-time monitoring of clinical laboratories contributing to clinical trials in oncology and in the real-world setting of HER2 immunohistochemistry testing in local clinical laboratories and hospitals.

And at the same journal, see "Introduction to Generative Artificial Intelligence; Contextualizing the Future," Singh et al., here.  [One of a series].

###

SIDEBAR:

AI Corner

Chat GPT took the agenda and wrote a fictional article about the conference "as if" in the voice of a journalist who had already attended it.   Find this here.  

Surviving a Medicare Audit: Some Updated Rules

 Medicare audits  take several different forms (MACs, RACs, etc), but most of the rules are specified in Chapter 3 of an online Program Integrity Manual.  CMS has issued some updates on December 18:

https://www.cms.gov/medicare/regulations-guidance/transmittals/2024-transmittals/r13008pi

It's called CR13735, T13008.

While most of the rules are unchanged, a few that are updated or if particular special interest are highlighted below.

  • Legal Authority to Audit Documents - 3.2.3.B
  • Third-Party Documents (e.g. audited lab is one party, ordering physician is a second party) - 3.2.3.3
    • (The lab is the first party, CMS is the second party, and an imaging center that holds a supporting MRI record is a "third party.")
  • Credentials of reviewers - 3.3.1.1.C
    • 3.3.1.1.B, Clinical review involves two steps, synthesis of all the submitted records, and, then, application "of this clinical picture" to review criteria [eg LCDs].
  • Automated Edits that Deny (LCDs, NCDs, MUE, etc) - 3.3.1.3.B

  • Special Rules for Psychotherapy notes - 3.3.2.6
  • Special Rules for (Physical) Therapy - 3.3.2.7
Records held by third-parties


Basis of auto denials



#
Recall in May 2024, MolDx issued an article noting that lab requisition forms are "part of the medical record"  which I believe means that information attested and signed on a lab requisition form does not need to be backed up (duplicated) by an earlier original medical chart record of the same fact.  E.g. I think that MolDx means, if the lab form says the patient has metastatic breast cancer, that IS a valid medical record, and the auditor ought not demand an earlier medical record document like an MRI report.  

Tuesday, December 17, 2024

Brief Blog: OIG Allows Biopharma to Pay for Certain Genetic Testing

There is always an area of legal caution and concern when biopharma pays for services, like genetic testing that is the gateway to a particular drug.

On December 17, 2024, OIG released an Advisory Opinion favorable to allow a biopharma to pay for genetics, and genetic counseling, related to its drug for a rare genetic cause of oxalate overproduction.

Find the report here:

https://oig.hhs.gov/documents/advisory-opinions/10117/AO-24-12.pdf

The OIG finds that the genetic services DO provide remuneration under the Anti Kickback Statute (AKS) and Beneficiary Inducement Civil Monetary Penalties (BI-CMP).   However, the OIG reasons that the risk of over use or improper use is very low and it would not seek penalties.

The case mentions that the lab is Blueprint Genetics, a subsidiary of Quest.   The case carefully redacts the name of the pharma and drug; the only rare genetic oxalate drug I know of is Oxlumo (lumasiran), from Alnylam (for primary hyperoxaluria Type 1 PH1).  On approving Oxlumo, FDA remarked, "PH1 affects an estimated one to three individuals per million in North America and Europe."  The drug is an RNAi therapeutic.

##
Oxlumo was approved in 2020 and got a label expansion in 2022 for plasma oxalate reduction in chronic kidney disease / CKD patients.

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Update. For context, see a January 6, 2025, briefing by Dark Report, that Ultragenyx paid a $6M fine in 2023 for testing for an ultra-rare gene. See an Op Ed in Stat, July 2024.  

Dark Report cites prior 2022 A.O. 22-06 from OIG (here).  However, the 2022 and 2024 OIG AO's are not directly contractory, per Chat GPT.  However, I asked Chat to compare the carefully worded AO's, to the $6M fine issued to Ultragenyx.   For that AI side bar see here.



Brief Blog: Register, Comment, for AMA CPT Meeting, San Jose, Feb 6-8, 2025

AMA has open registration (virtual or in person) for its upcoming CPT meeting, February 6-8, 2025.  The meeting will be in San Jose, CA.

While the special deadline for pathology test comments has passed, back on November 22, the comment period for other codes is open until January 7, 2025.

Review the agenda and access the registration info at the AMA webpage for the meeting:

https://www.ama-assn.org/member-benefits/events/cpt-editorial-panel-meeting

https://www.ama-assn.org/system/files/cpt-panel-february-2025-public-agenda.pdf

(The several proposed lab tests are at agenda items 20-25).

(Codes for epigenetic methylation (22) and lymph node metastases (25) are on deck.)

(A revision is proposed for a number of scattered codes that all represent "ultrasensitive immunoassays;" agenda 21).

(Upstream from pathology, 11 codes for prostate biopsy services are being revised; agenda 16.)



x
In October, 2024, AMA released "panel actions" from September 2024, no changes in pathology:







Article on Medicare "Breakthrough" Coverage: I Differ on Some Points

There's a new article on Medicare coverage of Breakthrough products (NTAP, TCET, MCIT, legislation).  It's in Health Affairs, and there is also an author commentary on Linked In by Dr. Lee Fleisher, prior chief medical officer at CMS.

Here's the article, Prasad et al.

https://www.healthaffairs.org/content/forefront/fda-breakthrough-device-designation-clinical-evidence-and-medicare-payment-policies

Here's Dr. Fleisher's context and comment:

https://www.linkedin.com/posts/lee-fleisher-b0779743_fda-breakthrough-device-designation-clinical-activity-7274468339580604417-x54A/?utm_source=share&utm_medium=member_ios

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I said I differed on a few points.   Here they are.

#1  NTAP isn't a coverage decision

Several years ago, Medicare (through notice and comment rule-making) eased the path to New Technology Add-on Payments for hospital stays, if the product had Breakthrough designation.   

But NTAP is a payment policy, not a coverage policy.  There have been devices that achieve NTAP yet collide with local LCDs (and, in principal, NCDs) that disallow payment.   There is no distinction at all in the coverage pathway for new devices with, or without, NTAP payment assignment.

#2 Trillions flow through general codes and DRGs

Through the ambulatory hospital APC system, the DRG system, and even in some cases the outpatient CPT system, new technologies with breakthrough status can often be paid at existing rates with no coverage review at all.   In fact, this is probably how the great majority of new products get paid.   

Yet this near-universal aspect of our general coding and payment system, through which trillions of dollars flow, gets no attention from the authors.   The amount of money flowing through these generic payment codes, with countless new technologies invisibly streaming along, surely dwarfs the amount of funds through policy features like NTAP.   CMS often reviews NTAP codes and finds they have only been used a handful of times for some NTAP products.   




Monday, December 16, 2024

Nerd Note: CMS Letter on Accelerated Approval and Med Adv Coverage

On December 9, CMS made a splash by posting a letter to Medicare Advantage plans, that they cannot classify an ALS drug (QALSODY) as "investigational" and therefore outside of covered drug benefits.   The drug is approved under FDA accelerated approval.

Here's an ALS press release,

https://www.als.org/stories-news/groundbreaking-directive-ensures-als-patients-medicare-advantage-gain-access-qalsody

Here's the CMS letter,

https://www.als.org/sites/default/files/2024-12/HPMS%20Memo_Qalsody%2012-9-24%201.pdf

The CMS letter states that CMS does NOT MAKE A DISTINCTION based on regular or accelerated approval.   (This has also been a talking point for former FDA commissioner Scott Gottlieb.)

Right?  Wrong?   Right...

When I read that "CMS does not make a distinction" based on accelerated approval, I thought, that's not literally true because the NCD on amyloid Alzheimer drugs treats accelerated approval and regular approval separately.   I mis-remembered.   Here's the policy:

https://www.cms.gov/medicare-coverage-database/view/ncd.aspx?ncdid=375#:~:text=Effective%20April%207%2C%202022%2C%20the,for%20patients%20who%20have%20a

The policy doesn't distinguish due to "accelerated approval" but rather, distinguishes because of drugs approved based on "surrogate endpoints."   OK, well, "Surrogate endpoints" is almost a synonym for "accelerated approval," but not quite.   In this NCD, the impact is that surrogate endpoints drugs must be covered only under a new RCT under an IND at the FDA.  That's a high bar.   On the other hand, drugs approved under "efficacy...clinical benefit" must only enroll in a simple registry.

So CMS doesn't literally distinguish, in this NCD, based on accelerated approval, although that was exactly the real impact.

##
This Dec 9 CMS letter re Medicare Advantage coverage reminds us there have been several years of complaints, policy-making, and new regulations trying to better define and enforce Med Adv coverage:

https://www.discoveriesinhealthpolicy.com/2024/12/cms-edits-medicare-advantage-coverage.html

##

Separately, CMS issued a Part D proposal regarding classifying obesity drugs like Wegovy as payable - this was a big splash and represents a position proposed by Biden and to be handled and finalized in the spring by DJT.   E.g. see here.

####

CMS can use NCDs to deal harshly with drugs approved on "surrogate endpoints," although we should remember there are some extremely well  accepted ones (e.g. vanishing of HIV viral titers, etc.).    That is, outside of the amyloid or ALS areas, a range of  "surrogate endpoints" have been associated with regular, traditional approvals:

https://www.fda.gov/drugs/development-resources/table-surrogate-endpoints-were-basis-drug-approval-or-licensure

For example, a drug for Cushing's disease (elevated cortisol) that causes cortisol to fall to normal levels.


Medicare Policy News: (1) CMMI Report and (2) Trends in Med Adv Benefits

Two stocking-stuffers for people who track Medicare policy.

First, Center for Medicare and Medicaid Innovation releases its 75-page annual report to Congress.   Recall that CMMI is a creation of the Affordable Care Act 2010, and has what reads like unlimited abilities to alter any Medicare regulation or law for the purpose of a demonstration project.  Both democratic and republican administrations have tried big things under that clause, such as altering national Part B drug payment policy.

https://www.cms.gov/priorities/innovation/data-and-reports/2024/rtc-2024

Because of its breadth, I've always wondered if the Supreme Court would strike its authority down as being an unwarranted or open-ended delegation of authority from Congress to an agency.  But I've not seen that argued.

Second, in Health Affairs, see an article by Cruse et al. on trends in Medicare Advantage supplemental benefits.  

https://www.healthaffairs.org/content/forefront/centering-beneficiaries-perspectives-amidst-change-medicare-advantage-supplemental


AI Corner

Chat GPT summarizes, concisely, the two articles.

CMS Innovation Center 2024 Report to Congress

The CMS Innovation Center's 2024 Report highlights its progress in testing payment and care delivery models to improve outcomes and reduce costs. Key strategies include advancing health equity, supporting safety net providers, and enhancing value-based care. New models like GUIDE for dementia care and AHEAD for state-based health equity reflect this focus. Evaluations emphasize scaling successful models, addressing health-related social needs (HRSNs), and fostering system transformation through multi-payer alignment and evidence-based innovation​.


Medicare Advantage Supplemental Benefits

Medicare Advantage Organizations (MAOs) are shifting toward flexible nonmedical benefits addressing health-related social needs (HRSNs) like food, housing, and transportation, often via flex cards. While LTSS-like benefits (e.g., in-home care) are declining, VBID and SSBCI models increasingly target chronically ill and low-income beneficiaries. Challenges include flex card administration, potential impacts on eligibility for public programs, and beneficiary access in rural areas. Policymakers must consider beneficiaries’ experiences when shaping future MA policies​.


Does the CMMI report contain any clues it was written by an outgoing Biden administration and that the next year's management will be under DJT?

The 2024 CMS Innovation Center Report to Congress does not contain overt signals that it was written by members of an outgoing Biden administration anticipating a Trump administration takeover. Instead, the tone remains forward-looking and nonpartisan, consistent with prior Innovation Center reports.

The report focuses on long-term goals, like achieving value-based care by 2030 and improving health equity, and highlights new models and bipartisan-supported strategies (e.g., addressing health-related social needs).

While it mentions significant progress during the current reporting period, there are no specific acknowledgments of a possible change in leadership, which is typical for federal agency reports during election cycles. If subtle signals exist, they might lie in:

  • Future model risks: Mentioning VBID's demonstration status and spillover effects, which could imply awareness of policy vulnerability under different administrations.
  • Neutral tone: A lack of partisan framing or overt alignment with administration-specific priorities.

However, such references are standard for federal reports and do not strongly indicate political foresight. If present, the signals are subtle and indirect, reflecting a cautious focus on continuity and evidence-based health reforms​

Thursday, December 12, 2024

LabCorp Promotes CGP, MRD, for BioPharma Trials

On the heels of recent final guidance from FDA on ctDNA in clinical trials, I noticed a  pair of splash-ads from Labcorp promoting biopharma use of its comprehensive genomic profiling CGP, Labcorp Tissue Complete, and its ctDNA test, Labcorp Plasma detect:


Tissue Complete "powereed by PGDx elio," leads to a four-page PDF:

https://content.labcorp.com/sites/default/files/2024-09/Labcorp%20Tissue%20Complete%20Infosheet_948650_0924.pdf

The ctDNA splash ad leads to a three-page PDF:

https://content.labcorp.com/sites/default/files/2024-09/Plasma%20Detect%20Infosheet_409950_0824.pdf

Labcorp seems to emphasize it spans 'end to end" services, it's not just a CDx kit manufacturer that produces a box.   The CGP service notes, for example, "While CGP holds immense potential to enhance the precision and effectiveness of oncology therapeutics, biopharmaceutical companies can benefit from Labcorp’s global capabilities across the entire product development life cycle. As an end-to-end partner from preclinical discovery through commercialization, we offer the insights and capabilities to help you bring the power of precision medicine to the patients who need it."

Medicare and New LCDs: Zippo for Two Months

 Each Thursday, CMS releases new LCDs (both proposed, and finaled) on its Medicare Coverage Database.

This week again, the page for new LCDs (released and in comment) is blank.


"Blank" means no documents released into their 45 day comment period, are still active.   The last release of a new draft LCD (by any MAC on any topic) was October 10.

###

A few draft LCDs were finaled this week - for MRI angiography, for MRI head & neck, for shoulder arthroplasty (L34424, L34425, L39956).


Friends of Cancer Research: Whole Slide Imaging, Her2, AI, and Pathologists' Eyes

One of my biggest blogs of 2022 asked whether low Her-2 slide readings would be a breakthrough moment for digital pathology (here).  

A major collaboration sponsored by Friends of Cancer Research (FoCR) was presented at San Antonio Breast Conference this week.   The upshot - the inter-rater agreement of whole slide imaging / AI (WSI-AI) was about as good as the inter-rater agreement of human experts.

  • Find the press release here.
  • Find the poster, McKelvey et al., here.
  • Here more results at the FoCR in person and virtual conference, February 4, 2025.   Here.
Here's a press release quote.

  • Washington, DC – December 12th, 2024 - New findings by Friends of Cancer Research (Friends) and collaborators were presented yesterday at the San Antonio Breast Cancer Symposium (SABCS), “Agreement Across 10 Artificial Intelligence Models in Assessing HER2 in Breast Cancer Whole Slide Images: Findings from the Friends of Cancer Research Digital PATH Project.”

  • The poster presented the agreement of HER2 biomarker assessment across independently developed computational pathology models. These preliminary findings suggest that the level of agreement of predicted HER2 scores across models is similar to published agreement measures across pathologists. 

  • “It is crucial to have accurate and consistent identification of patients who may benefit from targeted therapies such as antibody-drug conjugates (ADCs),” said Dr. Brittany Avin McKelvey, Director of Regulatory Affairs at Friends. “The Digital PATH Project’s unique collaborative approach enables us to explore the potential of AI models to deliver more quantitative and reproducible biomarker assessments and helps address the current lack of large-scale comparative evaluations of performance.” 

  • The Digital PATH Project launched in February 2024.
####

AI Corner

Chat GPT 4o summarizes the poster.

###

Summary: Agreement Across AI Models in HER2 Assessment for Breast Cancer

This poster from the Friends of Cancer Research Digital PATH Project evaluates the agreement across 10 independently developed artificial intelligence (AI) models in assessing HER2 status in breast cancer using whole slide images (WSIs).


Introduction

HER2-targeted therapies, including antibody-drug conjugates, have expanded the patient population benefiting from such treatments. Accurate HER2 scoring is critical, yet variability exists between pathologists. AI models offer a quantitative alternative, but their performance variability remains understudied.


Methods

  • Samples: WSIs (H&E and HER2 IHC) from 1,124 breast cancer patients (2021 cohort, ZAS Hospital, Belgium). 
    • HER2 scores were evaluated by three pathologists.
  • Models: 10 AI models from 9 developers were analyzed. Models varied in inputs and outputs (e.g., predicted ASCO/CAP scores, H-scores).
  • Analysis: Agreement was assessed without a defined reference standard using metrics such as Overall Percent Agreement (OPA) and Cohen’s kappa.

Results

  • Patient Cohort:

    • Median age: 65.
    • 94.3% with de novo diagnoses; 98.6% female.
    • Histology: 78.2% ductal, 15.3% lobular.
    • HER2 status: 51.6% HER2-negative, 48.4% HER2-low or positive.
  • Agreement Across Models:

    • Higher agreement for HER2 3+ (strongly positive) and 0 (negative) categories.
    • Greater variability for intermediate categories (1+ and 2+).
    • Agreement measures:
      • Categorical (0, 1+, 2+, 3+): Median OPA = 65.1%; Cohen’s kappa = 0.51.
      • Binary (e.g., 3+ vs. others): OPA = 97.3%; Cohen’s kappa = 0.86.
  • Figures:

    • Figure 1: Heatmap of HER2 scores by 7 models, showing clustering of samples with high or low HER2 scores.
    • Figure 2: Confusion matrix highlighting most disagreements between 1+ and 2+ scores.
    • Figure 3: Violin plots displaying pairwise agreement measures across models (OPA and kappa).

Conclusions

  • HER2 3+ cases showed the least variability and highest agreement.
  • Intermediate scores (1+, 2+) had significant inter-model variability.
  • Trends in AI model agreement mirror those seen among pathologists.

This study provides insight into variability across AI models and supports developing best practices for AI-driven biomarker assessments.


Next Steps

  • Deeper analyses on patient, specimen, and model characteristics.
  • Comparison of AI model outputs with pathologist readings.
  • Public meeting (February 4) to discuss findings, policy implications, and recommendations for reference sets.

This study reinforces the potential of AI models in clinical biomarker assessment while highlighting areas requiring standardization to ensure reproducibility and reliability. For details on data visualization, review Figures 1–3, which depict scoring trends and inter-model agreement.

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Sidebar.  Readers enjoying this topic might like,   Glass et al, 12-18-2024, Deployment of a Machine Learning Algorithm in a Real-World Cohort for Quality Control Monitoring of Human Epidermal Growth Factor-2–Stained Clinical Specimens in Breast Cancer, Arch Path Lab Med here.   


Wednesday, December 11, 2024

CMS Edits Medicare Advantage Coverage Rules: A Big Headache?

From the media to Congress, we hear about Medicare Advantage denials and roadblocks.  CMS now offers improvements to its rules, but some key facts may be out of place.

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Background - Recent and New Rulemaking

From postings at the AMA (here, here) to the halls of Congress (here), we hear about Medicare Advantage denial rates.  Some new proposed rules may be hiding some critical flaws.

Basically, Medicare Advantage plans have to at least match coverage in fee for service Part A and B, mostly as determined by NCDs and LCDs.  And for a long time, that's about all CMS wrote to describe the standard. [fn1]  

Then, in final rulemaking April 2023 (88 FR 22120), effective 2024, CMS issued pages of policy and paragraphs of regulations in the Federal Register.    

See also an important CMS explanatory memo in February 2024.

In a proposed rule published December 10, 2024, CMS revisits the Medicare Advantage coverage topic with several pages of policymaking and a few paragraphs of revised regulations.   Find it here, 89 FR 99340.  Comment is open til January 25, 2025.

What CMS Says

The particular recent rule is most famous for reasoning that newly, Part D plans must cover weight-loss drugs like Wegovy (99375ff, news here).  There's also an important section called "Guardrails for AI" in plan decision-making (99396ff; blog here).   But in this article, I'm drawing attention to Coverage Criteria for Medicare Advantage (99455-461, plus regs at 422.101 (99557-8).  

CMS states that Medicare Advantage continues to be a source of confusion and misunderstandings, requiring new explanations and rules to ensure beneficiary coverage.  

They focus most strongly on language in NCDs and LCDs.  CMS states that these always have clear-cut coverage rules, because any gaps or lack in clarity would have been fixed during public comment.  (Hope you were sitting down.)  

M.A. plans need only fall the "plain language" of the library of LCDs and NCDs.  CMS also emphasize that they mean exactly the "LCDs" because coding/billing articles for an LCD "do not contain coverage criteria, that is the role of the LCD."

OMG... I See Problems

This leaves me wondering if authors of the rule know the Part B world at all.   For example, the proposed oncology LCD from Novitas, which is suspended without finalization, is a verbose, repetitive, confusing morass - hardly "plain language."  (Here).  And it would be impossible to guess its implementation without reference to a huge billing and coding article with over 100 sections, arranged willy-nilly.

A different problem arises in the 28 MolDx states, which have the lion's share of all MoPath payments in Medicare.   MolDx issues "foundational LCDs" which give general principles for coverage but the LCD never says what conditions, which cancers, or which tests are covered.   Rather, this might be found in the current billing article, or, in the proprietary Palmetto MolDx DEX Database (here).     

The Palmettogba.com/MolDx webpage even includes a notice that billing and coding articles (in CMS format) will often lack information on tests and services because this can be found over at the separate DEX registry.  (Screen shot from here, then here; taken 12-11-2024).  

  • Topic is the LCD supplements aka Articles, with tables of billing and coding data, 
  • "These tables are being removed because coverage information on explicit services that have met coverage criteria can be readily found in the DEX™ Registry".


L38779, MolDx, Minimal Residual Disease

For example, in one of MolDx's most important policies, on minimal residual disease testing in cancer L38779, although there are 10 coverage rules, many are self-explanatory (the patient has a history of cancer.)  Coverage criteria are presented in general terms (the identification of cancer would lead to a change in management; or stating that the test is demonstrated to identify recurrence berfore there is other evidence.)   It would be very hard for a Medicare Adantage plan - or better, for ten or twenty different Medicare Advantage plans - to read those general expressions and all tens exactly and identically know whether a particular test (say, the LabCo test when used in liver cancer when used for completeness of curative resection) is or is not currently covered by Medicare fee-for-service.  

And unlike coverage codes tied to CMS-based LCD articles, in DEX, it's impossible to search for tests covered or not covered by any given LCD, to know at one view which MRD tests are covered and which not, nor to know dates of coverage (no dates in DEX).

Bringing It Home:  Real World versus the Proposed Medicare Advantage Rule

So if I read the CMS rule correctly, it points to the LCD alone as the near-Biblical definition of coverage.   And - just like the Bible  - the LCDs and NCDs are so clear they can be uniformly interpreted the same way by everybody just by their "plain text" (CMS's term of art).  

So this is the status quo of LCD and Article:

OK, that was familiar.

This new guidance for Medicare Advantage decisions is focused on solely the "plain text" of each LCD:

I've put the Red X, because CMS says coverage info needed by Medicare Advantage won't be, shouldn't be, sought in articles, it is only in the LCDs and by quickly reading their "plain text."

That's even further from the coverage rules needed in the MolDx sphere, where the DEX database (plus Z code descriptors) hold some of the key information.  As we quoted earlier, "coverage information on explicit [particular] services that have met coverage criteria can be readily found in the DEX™ Registry"

Again, contrast that to the propose rule, we get the Red X over articles or the DEX, because CMS says that Medicare Advantage plans need solely interpret the "plain text" of LCDs.   

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AI Corner

Google Notebook LM reads this article.

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CMS Proposed Rule for Medicare Advantage Coverage: A Recipe for Confusion?


This briefing document reviews a December 10, 2024 proposed rule from the Centers for Medicare & Medicaid Services (CMS) concerning Medicare Advantage coverage criteria, as analyzed by healthcare policy expert Bruce Quinn, MD, PhD, in his blog post "CMS Edits Medicare Advantage Coverage Rules: A Big Mess?" (December 11, 2024).


Main Theme: The proposed rule simplifies Medicare Advantage coverage determination by emphasizing the "plain language" of National Coverage Determinations (NCDs) and Local Coverage Determinations (LCDs) as the sole source of truth. However, Quinn argues that this approach ignores real-world complexities in Part B coverage, particularly in specialized areas like molecular diagnostics, creating potential for confusion and inconsistent coverage decisions among Medicare Advantage plans.


Key Points and Facts:

  • Background: The proposed rule follows previous efforts by CMS to clarify Medicare Advantage coverage requirements, amidst concerns over denials and barriers to care.
  • Focus on "Plain Language" of LCDs and NCDs: The proposed rule asserts that NCDs and LCDs provide clear coverage rules, with any ambiguity resolved during public comment periods. Medicare Advantage plans are expected to adhere to these documents as the definitive guide for coverage decisions.
  • Quinn's Critique: Quinn challenges this assertion, highlighting the often convoluted and unclear language of LCDs, citing a pending oncology LCD from Novitas as an example. He also argues that relying solely on LCDs ignores the crucial role of:
  • Coding/billing articles: Quinn points out that these articles often contain essential coverage details not found in the LCD itself. He specifically cites the Palmetto MolDx program, where billing articles are explicitly skipped (per a website notification), and coverage information for specific tests resides instead in a separate, proprietary database (DEX).
  • MolDx policies: Using the example of minimal residual disease (MRD) testing in cancer (L38779), Quinn demonstrates how general coverage statements in the LCD require interpretation based on specific test applications and clinical contexts, information often found in the DEX database or the wording of Z code descriptors.
  • Disparity between Proposed Rule and Reality: Quinn argues that the proposed rule's reliance on "plain text" interpretation of LCDs creates a disconnect with the realities of Part B coverage, particularly in complex, evolving areas like molecular diagnostics. This could lead to inconsistent coverage decisions across different Medicare Advantage plans, disadvantaging beneficiaries.


Key Quotes:

  • CMS on NCDs and LCDs: "CMS states that these always have clear-cut coverage rules, because any gaps or lack in clarity would have been fixed during public comment."
  • Quinn on LCD Complexity: "For example, the proposed oncology LCD from Novitas, which is suspended without finalization, is a verbose, repetitive, confusing morass - hardly 'plain language.'"
  • Quinn on the Role of Coding Articles and DEX: "The Palmettogba.com/MolDx webpage even includes a notice that billing and coding articles (in CMS format) will often lack information on tests and services because this can be found over at the separate DEX registry."
  • Quinn on Potential for Inconsistent Coverage: "It would be very hard for a Medicare Advantage plan - or better, for ten or twenty different Medicare Advantage plans - to read those general expressions and all tens exactly and identically know whether a particular test [...] is or is not currently covered by Medicare fee-for-service."


Conclusion:

Quinn suggests that the proposed rule's oversimplification of Medicare Advantage coverage determination, while aiming for clarity, risks exacerbating existing confusion and potentially hindering beneficiary access to care. He advocates for a more nuanced approach that recognizes the complexities of Part B coverage and the need for additional resources beyond the "plain text" of LCDs

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I'm highlighting MolDx here because of its elaborate system of LCDs, articles, and DEX database all interacting with one another to give a picture of coverage, but even then, often only partial (e.g. there may be no definitions of frequency for "use case X" when surveillance is involved).

But this requirement for interpretation isn't limited.  Take an NCD.  CMS NCD 90.2 governs next generation sequencing used in cancer patients, and has some maddening ambiguities that were not fixed during the comment period, or  new strangeness was introduced by CMS's new text appearing only in the final NCD.  And there is a baroque, huge, Excel spreadsheet with many pages of lengthy instructions about what is covered or not covered, updated quarterly (see CR13596.zip within prior link).   To refer to the "plain text" of the NCD assuming all the Medicare Advantage parties would interpret it the same way 8 years later (without the years of supplements and coverage documents) is a false hope.

Another example of vagueness is the newly updated MRI LCD (L34425) for head and deck.  The coverage section spends half its time simply defining MRI "radiofrequency signals when exposed to radio waves," and explaining that various metal fragments and clips may be contraindications.  Then, it states that MRI of the orbit, face, and neck may be medically necessary to diagnose and characterize pathology of the orbit, face, and neck.   (OK, thanks, guys).   

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fn1

These new rules and policymaking complexify old legacy simple statements about coverage parity which are still found in Medicare Managed Care Manual, Chapter 4, section 10.2.


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Here's another example of very detailed NCD implementation rules that one would be hard-pressed to extract "from the NCD" which is concise.

Monday, December 9, 2024

Brief Blog: Where Are the NCCI Edits for 2025?

UPDATE

Posted December 13.   Basically nothing to see, in laboratory medicine.

https://www.cms.gov/medicare/coding-billing/national-correct-coding-initiative-ncci-edits/medicare-ncci-policy-manual

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One of the persistent complaints of the lab industry, to CMS, has been the secretive process for National Correct Coding Initiative edits, their errors, and how they are sprung at the end of the year on the provider community.   (ACLA has even complained to other parts of the government, about these edits, here.)

2025 is no better.   Last year, the edit policy manual came out on December 1 (for 2024).   Right now, it's December 9 and the new 2025 manual is not released yet.

Track its appearance at this webpage:

https://www.cms.gov/medicare/coding-billing/national-correct-coding-initiative-ncci-edits/medicare-ncci-policy-manual