Patient Coded Severity and Payment Penalties Under the Hospital Readmissions Reduction Program: A Machine Learning Approach. Issue 11 (November 2020)

• Record Type: Journal Article
• Authors: Li, Jun; Sukul, Devraj; Nuliyalu, Ushapoorna; Ryan, Andrew M.
• Is Part Of: Medical care. Volume 58:Issue 11(2020)
• Publication Date: 2020-11
• Subjects: pay-for-performance -- value-based purchasing programs -- patient severity -- risk adjustment -- coding; Economics, Medical -- Periodicals; Insurance, Health -- Periodicals; Santé, Services de -- Administration -- Périodiques; Soins médicaux -- Périodiques; Medical economics -- Periodicals; Health insurance -- Periodicals; Medical economics -- United States -- Periodicals; Health insurance -- United States -- Periodicals; Comprehensive Health Care -- Periodicals; Personal Health Services -- Periodicals; Gezondheidszorg; Économie de la santé -- Périodiques; Santé, Services de -- Périodiques; Health insurance; Medical economics; United States; Periodicals; 362.10973
• DOI: 10.1097/MLR.0000000000001396
• Languages: English
• ISSNs: 0025-7079

Abstract

Abstract : Objective: The objective of this study was to examine variation in hospital responses to the Centers for Medicare and Medicaid's expansion of allowable secondary diagnoses in January 2011 and its association with financial penalties under the Hospital Readmission Reduction Program (HRRP). Data Sources/Study Setting: Medicare administrative claims for discharges between July 2008 and June 2011 (N=3102 hospitals). Research Design: We examined hospital variation in response to the expansion of secondary diagnoses by describing changes in comorbidity coding before and after the policy change. We used random forest machine learning regression to examine hospital characteristics associated with coded severity. We then used a 2-part model to assess whether variation in coded severity was associated with readmission penalties. Results: Changes in severity coding varied considerably across hospitals. Random forest models indicated that greater baseline levels of condition categories, case-mix index, and hospital size were associated with larger changes in condition categories. Hospital coding of an additional condition category was associated with a nonsignificant 3.8 percentage point increase in the probability for penalties under the HRRP (SE=2.2) and a nonsignificant 0.016 percentage point increase in penalty amount (SE=0.016). Conclusion: Changes in patient coded severity did not affect readmission penalties. Abstract : Supplemental Digital Content is available in the text.