Patient coded severity and payment penalties under the hospital readmissions reduction program: A machine learning approach

Jun Li, Devraj Sukul, Ushapoorna Nuliyalu, Andrew M. Ryan

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1022-1029
Number of pages8
JournalMedical Care
Volume58
Issue number11
DOIs
StatePublished - Nov 1 2020

Keywords

  • coding
  • patient severity
  • pay-for-performance
  • risk adjustment
  • value-based purchasing programs

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

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