The Imperfect Science of Evaluating Performance: How Bad and Who Cares?
Authors: Aaron L. Schwartz, Rachel M. Werner
Journal: Annals of Internal Medicine (2022)

EditorialsMarch 2022The Imperfect Science of Evaluating Performance: How Bad and Who Cares?Aaron L. Schwartz, MD, PhD and Rachel M. Werner, MD, PhDAaron L. Schwartz, MD, PhDDepartment of Medical Ethics and Health Policy and Division of General Internal Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Crescenz VA Medical Center, and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PennsylvaniaSearch for more papers by this author and Rachel M. Werner, MD, PhDDivision of General Internal Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Crescenz VA Medical Center, and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PennsylvaniaSearch for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M21-4665 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Rewarding performance will improve performance. This principle has driven a wide range of reforms aimed at fostering accountability for outcomes in the U.S. health care system—from report cards on provider quality to risk-based contracts between insurers and providers. However, rewarding performance raises the stakes for accurately evaluating performance, a daunting task. Inaccurate measurement could encourage wasteful efforts to boost scores, reduce access to care for sick patients, and promote mistrust.A key to accurately measuring the performance of providers or insurers is accounting for differences in patient risk. Risk adjustment is the standard statistical tool for leveling the playing field; ...References1. Wallace J, McWilliams JM, Lollo A, et al. Residual confounding in health plan performance assessments: evidence from randomization in Medicaid. Ann Intern Med. 2022;175:314-24. doi:10.7326/M21-0881 Google Scholar2. Irvin JA, Kondrich AA, Ko M, et al. Incorporating machine learning and social determinants of health indicators into prospective risk adjustment for health plan payments. BMC Public Health. 2020;20:608. [PMID: 32357871] doi:10.1186/s12889-020-08735-0 CrossrefMedlineGoogle Scholar3. Finkelstein A, Gentzkow M, Hull P, et al. Adjusting risk adjustment—accounting for variation in diagnostic intensity. N Engl J Med. 2017;376:608-610. 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[PMID: 29465215] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: Department of Medical Ethics and Health Policy and Division of General Internal Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Crescenz VA Medical Center, and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PennsylvaniaDivision of General Internal Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Crescenz VA Medical Center, and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PennsylvaniaDisclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M21-4665.Corresponding Author: Rachel M. Werner, MD, PhD, University of Pennsylvania, Colonial Penn Center, Room 210, 3641 Locust Walk, Philadelphia, PA 19104; e-mail, [email protected]edu.This article was published at Annals.org on 4 January 2022. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoResidual Confounding in Health Plan Performance Assessments: Evidence From Randomization in Medicaid Jacob Wallace , J. Michael McWilliams , Anthony Lollo , Janet Eaton , and Chima D. Ndumele Metrics March 2022Volume 175, Issue 3Page: 448-449KeywordsPerformance measuresRisk adjustments ePublished: 4 January 2022 Issue Published: March 2022 Copyright & PermissionsCopyright © 2022 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...