Evidence map›Paper›PMID 42729961›Full record

ArticleHealth affairs scholar2026

Streamlining oversight in Medicare advantage: measuring coding intensity via contract rankings.

Trenton Chang, Michelle Qiu, Zhiyi Hu, Dylan Zapzalka, Divya M Shanmugam, Daniel K Shenfeld, Ravi B Parikh, Maggie Makar, Ezekiel J Emanuel, Zach Brown and 2 more

Abstract read
In one paragraph

Article in Health affairs scholar, 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
–field-weighted citation impact
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.

Trenton ChangDivision of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, United States.ORCID https://orcid.org/0000-0003-4679-5841
Michelle QiuDivision of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, United States.ORCID https://orcid.org/0000-0002-1758-4175
Zhiyi HuDivision of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, United States.ORCID https://orcid.org/0009-0009-9737-5717
Dylan ZapzalkaDivision of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, United States.ORCID https://orcid.org/0009-0009-3719-2847
Divya M ShanmugamDepartment of Computer Science, Cornell Tech, New York, NY 10044, United States.
Daniel K ShenfeldDepartment of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, PA 19104, United States.ORCID https://orcid.org/0009-0000-4445-6999
Ravi B ParikhWinship Cancer Institute, Emory University, Atlanta, GA 30322, United States.ORCID https://orcid.org/0000-0003-2692-6306
Maggie MakarDivision of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, United States.ORCID https://orcid.org/0000-0002-1224-4657
Ezekiel J EmanuelPerelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States.ORCID https://orcid.org/0000-0002-9796-5735
Zach BrownDepartment of Economics, University of Michigan, Ann Arbor, MI 48109, United States.
Amanda E KowalskiDepartment of Economics, University of Michigan, Ann Arbor, MI 48109, United States.ORCID https://orcid.org/0000-0002-0221-8826
Jenna WiensDivision of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, United States.ORCID https://orcid.org/0000-0002-1057-7722

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Risk-adjusted payments in Medicare Advantage (MA) incentivize insurers to maximize reported diagnoses, resulting in high coding intensity, a major source of Medicare overpayments. However, distinguishing justifiable coding intensity from misreporting is challenging: some approaches risk conflating coding intensity due to extraneous diagnoses with that due to variation in clinical practice or beneficiary health, while others assume specific overreporting mechanisms. Methods: We propose a data-driven method for ranking MA contracts by coding intensity using comparisons between geographically co-located contracts based on adjusted diagnosis rates among switchers from Traditional Medicare to MA. The approach is applied to a 20% sample of 2019 MA claims. Results: Health Maintenance Organization (HMO)-dominated contracts tend to rank higher than preferred provider option (PPO)-dominated contracts, echoing previous findings. Furthermore, highly ranked contracts administer procedures and prescriptions relevant to beneficiaries' diagnoses less often than lower-ranked contracts, supporting the validity of the ranking. A simulation study suggests even an imperfect ranking can recover overpayments faster than a random audit, if mean diagnosis rates are low (10%) and vary widely across contracts. Conclusion: As MA looks to expand auditing strategies, MA enrollment continues to grow. The proposed approach can facilitate payer-specific audits even as mechanisms driving coding intensity evolve.

Indexed as

auditingcoding intensitymedicareMedicare Advantagepayment policypayment reform

Identifiers

PMID42729961
PMCPMC13564226

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.