Evidence map›Paper›PMID 42826029›Full record

ArticleJAMA health forum2026

Service-Level Utilization in Risk-Based Contracts and a Benchmarking Approach for Fee-Schedule Reforms.

Aaron L Schwartz, Timothy Bulat, Eli Ben-Michael, Erkuan Wang, Jingsan Zhu, Emily Boudreau, Sumedha Chhatre, Soohyun Kim, David Pagnotti, Sara Riaz and 2 more

Abstract read
In one paragraph

Article in JAMA health forum, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Aaron L SchwartzDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia.
Timothy BulatDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia.
Eli Ben-MichaelDepartment of Statistics and Data Science, Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania.
Erkuan WangDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia.
Jingsan ZhuDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia.
Emily BoudreauHumana Healthcare Research, Louisville, Kentucky.
Sumedha ChhatreDepartment of Population Health Sciences, John D. Bower School of Population Health, University of Mississippi Medical Center, Jackson.
Soohyun KimDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia.
David PagnottiDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia.
Sara RiazCollege of Osteopathic Medicine, Michigan State University, East Lansing, Michigan.
Amanda SutherlandHumana Healthcare Research, Louisville, Kentucky.
Amol NavatheDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Despite revived interest in reforming the physician fee schedule, it is unclear how policymakers should modify more than 1000 different physician service prices to promote high-value care. One systematic approach could leverage service-level utilization patterns in full-risk contracts, which have not been previously characterized. Objective: To compare service-level utilization patterns between health care professional organizations in full-risk contracts and those in a traditional fee-for-service (FFS) payment structure, adjusted for detailed patient-level differences, and to simulate an FFS fee schedule reform that would encourage the service patterns observed in risk-based contracts. Design, Setting, and Participants: This retrospective cross-sectional study used Humana Medicare Advantage claims, encounter, and administrative data from 2015 to 2019 for 585 487 beneficiaries attributed to full-risk contract, with 100% downside risk for total medical spending, and 1 153 455 beneficiaries in fee-for-service contract arrangements. Data were analyzed from September 2025 to June 2026. Main Outcomes and Measures: The primary outcome was service-specific utilization (count per beneficiary-year), adjusted for beneficiary characteristics. A secondary analysis simulated a modified Medicare FFS schedule based on estimated service-level utilization differences and a supply elasticity drawn from prior literature. Results: Among 1271 services examined, 940 (74%) had lower adjusted utilization in the risk cohort, and 331 (26%) had higher adjusted utilization. For 700 services (55%), adjusted utilization was at least 10% less in the full-risk cohort. For 199 services (16%), adjusted utilization was at least 10% greater. Low-level office visits and laboratory services were more common in risk-based settings, whereas higher-level office visits, hospital and emergency services, and rehabilitative therapies were less common. A simulated budget-neutral reform to the Medicare fee schedule, calibrated to shift utilization levels toward risk-based benchmarks, yielded a possible 6% increase in reimbursement to general practice physicians. Conclusion and Relevance: In this cross-sectional study, the association between risk-based contracts and service utilization varied substantially across 1271 individual services. Service-specific utilization levels in risk-based contracts may serve as a benchmark to guide reforms to fee-for-service prices.

Indexed as

BenchmarkingFee-for-Service PlansFee SchedulesAgedContractsCross-Sectional StudiesFemaleHumansMaleRetrospective StudiesUnited StatesValue-Based Health Care

Identifiers

PMID42826029
PMCPMC13633545

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.