Eric T. Roberts, PhD; Alan M. Zaslavsky, PhD; J. Michael McWilliams, MD, PhD
Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Laura and John Arnold Foundation.
Grant Support: By grants from the Laura and John Arnold Foundation, National Institute on Aging of the National Institutes of Health (P01 AG032952), and Marshall J. Seidman Center for Studies in Health Economics and Health Care Policy at Harvard Medical School.
Disclosures: Dr. McWilliams reports personal fees from Abt Associates and the Medicare Payment Advisory Commission outside the submitted work. Authors not named here have disclosed no conflicts of interest. Disclosures can also be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M17-1740.
Editors' Disclosures: Christine Laine, MD, MPH, Editor in Chief, reports that she has no financial relationships or interests to disclose. Darren B. Taichman, MD, PhD, Executive Deputy Editor, reports that he has no financial relationships or interests to disclose. Cynthia D. Mulrow, MD, MSc, Senior Deputy Editor, reports that she has no relationships or interests to disclose. Deborah Cotton, MD, MPH, Deputy Editor, reports that she has no financial relationships or interest to disclose. Jaya K. Rao, MD, MHS, Deputy Editor, reports that she has stock holdings/options in Eli Lilly and Pfizer. Sankey V. Williams, MD, Deputy Editor, reports that he has no financial relationships or interests to disclose. Catharine B. Stack, PhD, MS, Deputy Editor for Statistics, reports that she has stock holdings in Pfizer and Johnson & Johnson.
Reproducible Research Statement:Study protocol: As described in Methods and the . Statistical code: Available from Dr. Roberts (e-mail, firstname.lastname@example.org). Data set: Available from CMS under its procedures for research data.
Requests for Single Reprints: J. Michael McWilliams, MD, PhD, Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston, MA 02115; e-mail, email@example.com.
Current Author Addresses: Dr. Roberts: Department of Health Policy and Management, University of Pittsburgh Graduate School of Public Health, A653 Crabtree Hall, 130 De Soto Street, Pittsburgh, PA 15261.
Drs. Zaslavsky and McWilliams: Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston, MA 02115.
Author Contributions: Conception and design: E.T. Roberts, A.M. Zaslavsky, J.M. McWilliams.
Analysis and interpretation of the data: E.T. Roberts, A.M. Zaslavsky, J.M. McWilliams.
Drafting of the article: E.T. Roberts, J.M. McWilliams.
Critical revision for important intellectual content: E.T. Roberts, A.M. Zaslavsky, J.M. McWilliams.
Final approval of the article: E.T. Roberts, A.M. Zaslavsky, J.M. McWilliams.
Statistical expertise: A.M. Zaslavsky, J.M. McWilliams.
Obtaining of funding: J.M. McWilliams.
Collection and assembly of data: E.T. Roberts, J.M. McWilliams.
When risk adjustment is inadequate and incentives are weak, pay-for-performance programs, such as the Value-Based Payment Modifier (Value Modifier [VM]) implemented by the Centers for Medicare & Medicaid Services, may contribute to health care disparities without improving performance on average.
To estimate the association between VM exposure and performance on quality and spending measures and to assess the effects of adjusting for additional patient characteristics on performance differences between practices serving higher-risk and those serving lower-risk patients.
Exploiting the phase-in of the VM on the basis of practice size, regression discontinuity analysis and 2014 Medicare claims were used to estimate differences in practice performance associated with exposure of practices with 100 or more clinicians to full VM incentives (bonuses and penalties) and exposure of practices with 10 or more clinicians to partial incentives (bonuses only). Analyses were repeated with 2015 claims to estimate performance differences associated with a second year of exposure above the threshold of 100 or more clinicians. Performance differences were assessed between practices serving higher- and those serving lower-risk patients after standard Medicare adjustments versus adjustment for additional patient characteristics.
Random 20% sample of beneficiaries.
Hospitalization for ambulatory care–sensitive conditions, all-cause 30-day readmissions, Medicare spending, and mortality.
No statistically significant discontinuities were found at the threshold of 10 or more or 100 or more clinicians in the relationship between practice size and performance on quality or spending measures in either year. Adjustment for additional patient characteristics narrowed performance differences by 9.2% to 67.9% between practices in the highest and those in the lowest quartile of Medicaid patients and Hierarchical Condition Category scores.
Observational design and administrative data.
The VM was not associated with differences in performance on program measures. Performance differences between practices serving higher- and those serving lower-risk patients were affected considerably by additional adjustments, suggesting a potential for Medicare's pay-for-performance programs to exacerbate health care disparities.
The Laura and John Arnold Foundation and National Institute on Aging.
Roberts ET, Zaslavsky AM, McWilliams JM. The Value-Based Payment Modifier: Program Outcomes and Implications for Disparities. Ann Intern Med. ;168:255–265. doi: 10.7326/M17-1740
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Published: Ann Intern Med. 2018;168(4):255-265.
Published at www.annals.org on 28 November 2017
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