Patient Coded Severity and Payment Penalties Under the Hospital Readmissions Reduction Program: A Machine Learning Approach.

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 se

SUBMITTER: Li J 

PROVIDER: S-EPMC7572594 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature