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ACO Quality Reporting Options (2x2)

Original on LinkedIn

As I come up to speed in a new role, I’m learning a lot about how ACOs collect clinical data, calculate quality measures for CMS, and report them. The rules are… complex, especially for a newcomer. And there are multiple options with distinct requirements and implications. This article is just consolidating my nascent understanding of the reporting options; I’d welcome any feedback and (especially) corrections!

Background

The ACO program exists to promote value-based care — i.e. helping primary care providers to drive down the cost of care while improving quality and patient experience. ACOs that achieve high quality are eligible for their full share of any financial savings (or a reduced share of any losses if costs come in above the benchmark).

“APP Plus” is the quality-measure set that Medicare Shared Savings Program ACOs submit to CMS to determine this “quality”. It replaced the prior APP set beginning in 2025, focusing on a smaller primary-care core (chronic-disease control, preventive screening, and behavioral health) alongside claims-based outcomes and patient experience. In 2026, eight measures are in the set; five require ACO-submitted clinical results.

Under the CY 2027 proposal, the reporting options for these clinical measures map into this 2×2:

Reporting based on an all-payer/all-patient population can qualify for a “reporting incentive”: if all five clinical measures are reported at the population level and clear some de minimus thresholds for completeness and performance, then CMS “deems” the ACO to meet the quality performance standard. This unlocks full shared savings rates for the ACO.

Reporting based on assigned patient population (roughly: just your Medicare patients) would allow you to compute your quality against “flat benchmarks”, meaning your quality scores are based linearly on your measure rates rather than “graded on a curve” against other providers.

(If this all seems a bit complicated — yes! Teaser in this post’s banner image: I’m working on a little open source simulator with a visual language for thinking about these concepts.)

… and an asymmetric bonus

The “Complex Organization Adjustment” (COA) is a special bonus available only if you use eCQM to report on the full population:

The COA adds a point to your measure score for every measure you’ve reported as a full-population eCQM. For ACOs using a mix of reporting options for different measures, this can help boost scores in a regime where every point improves the shared savings rates (up to the quality standard, which acts as a ceiling). Even for ACOs reporting all five measures as full-population eCQMs (which ~automatically meet the quality standard and therefore get full shared savings by virtue of “deeming”), a higher score can limit mitigate expose to shared losses (down to a 40% floor).

(The raw score can also be important as a claims-fee-multiplier for providers that don’t meet the definition of “qualified providers” within their ACO — but this too far in the weeds for an introductory post, and the proposed rules leave the details in flux.)

Submitting quality results

Submission of quality measure results is a separate layer, independent from the selected reporting options. CMS permits APP quality data in QPP JSON or QRDA III; the direct QPP Submission API always uses JSON.

Sources

Conversation on LinkedIn

12 comments · 51 reactionsReply on LinkedIn
  • Wayne SingerAug 7, 2026

    Senior Vice President - Regulatory Affairs at Darena Solutions

    Josh,
    Welcome to the wonderful world of MIPS and value-based care, where nothing is ever quite as simple as it first appears! Really nice framework for making a complicated set of ACO reporting choices easier to understand. One thing I would add is the operational reality that for many ACOs, the real challenge is consistently aggregating complete, measure-ready data across multiple EHRs and participant TINs. In practice, data completeness and interoperability can influence the reporting choice just as much as the scoring methodology. Great perspective and a great way to frame the discussion.

    3 reactions

    • Josh Mandel, MDAuthorAug 8, 2026

      Thanks -- and yes, thus dynamic plays out in my simulator where "extraction completeness" is a specific setting with very clear (and important) consequences.

    • Judson P. BrunoAug 17, 2026

      Healthcare Product & Growth Leader | Patient-Controlled Health Data | FHIR, SMART Health Links & AI | Kill the Clipboard | HealthScores.ai

      Wayne Singer, Josh Mandel, MD, is a valuable part of the CMS "Kill the Clipboard." He has a knack for seeing ideas before requirements and then putting them in place.

  • Jeremy HuynhAug 8, 2026

    Clinical Pharmacist at Prime Therapeutics

    So you’re saying missing data is the thing holding scores down; data access is the thing that raises them?

    • Wayne SingerAug 8, 2026

      Senior Vice President - Regulatory Affairs at Darena Solutions

      Jeremy Huynh Exactly, with one important distinction. Missing data can hurt the score, but access alone doesn’t improve it. The key is getting complete, timely data into a form that can identify gaps and drive action before reporting.

      1 reaction

    • Jeremy HuynhAug 8, 2026

      Clinical Pharmacist at Prime Therapeutics

      Wayne Singer so if the value is to “identify the gap and drive the action in time,” and whoever owns that loop owns the thing everyone’s actually being paid for, then for the ACOs for SNFs, the SNFs are the owner of the systems of record the only way to get timely info is if they give the ACOs permission/access to their data?

  • Healthcare Analytics Leader | Value-Based Care & Medical Economics | Medicare, Claims, Attribution & Provider Performance

    Really helpful framing, Josh Mandel, MD Separating the reporting population, scoring methodology, and submission mechanics makes a complicated topic much easier to reason through. Looking forward to seeing how the simulator develops.

  • Mark OswaldAug 8, 2026

    Experienced Data and AI Architect. I’m passionate about using Data and AI to solve problems, create opportunities, and innovate within the Healthcare and Life Sciences industries.

    Welcome to the World of VBC and it's interesting collection of problems/solutions.

    Given your interest in simulation, you might be interested in looking at Vantage - https://github.com/mark64oswald/vantage-docs.

    Vantage is a simulation tool that can generate populations that mirror your own, and then model four interconnected outcome domains:
    ◾ Risk Adjustment (V24/V28 scoring, RAF impact)
    ◾ Stars & Quality (competitive scoring with dynamic cut-points)
    ◾ Medical Cost / MLR (integrated P&L projections)
    ◾ RADV Audit Exposure (the liability side most platforms ignore)

    From there, you can manipulate a set of six connected intervention levers — care management, coding initiatives, formulary design, PA policy, provider contracts, benefit design — and show how each change ripples across every domain simultaneously.

    If you read through the public documentation and would like to explore deeper or collaborate, I'd be happy to make the rest of it public.

  • Elena RodinAug 11, 2026

    AI systems that survive payer audits and clinical review | Prior auth, UM, claims, clinical AI | Built for production, not demos.

    A simulator earns its keep only if it's pinned to the spec version the payer will audit against, not the one currently published. Reporting year logic versus audit year logic is where the reconciliation surprises live. Can it replay a measurement year under the spec as it stood mid year, before a revision changed denominator eligibility?

    • Josh Mandel, MDAuthorAug 11, 2026 · edited

      I know this is just an ai-generated comment but for the benefit of other readers: this is not the level at which my simulator operates :-) It's a didactic tool where the inputs are just raw "numerator / denominator" rates -- it doesn't *calculate* numerators or denominators.

      1 reaction

    • Elena RodinAug 11, 2026

      AI systems that survive payer audits and clinical review | Prior auth, UM, claims, clinical AI | Built for production, not demos.

      You got me, Josh. I commented off the summary without looking at the tool itself, and it shows. Didactic with raw rates makes way more sense for what you're doing. Carry on :)

  • Josh Mandel, MDAuthorAug 11, 2026

    Update: simulator post live at https://www.linkedin.com/feed/update/urn:li:activity:7492546574942629888/