Once A Cure; Second A Waste

Examining Hospital Readmission Rates of the Commercially Insured Under 65 Population

by Ning Fu

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Unnecessary hospital readmissions indicate poor health care quality and lead to substantial waste of health care resources. The recently introduced Medicare Hospital Readmission Reduction Program that penalizes hospitals with excessive hospital readmissions of Medicare beneficiaries has drawn greater attention to hospital readmissions from policy makers, health care payers and providers, and academics. However, commercially insured patients under the age of 65 also represent a significant part of the U.S. healthcare market and little is known yet about this population. Using a unique dataset that covers inpatient hospital admissions of a population of commercially insured patients under age 65 from California during 2003-2012, this dissertation makes contributions to the knowledge gap in the literature.

Based on a series of empirical tests and sensitivity analyses comparing the readmission rates calculated using different risk adjustment methods currently in use and a new method proposed in this study, this dissertation provides evidence that adding patients' primary diagnosis indicators as 30-day readmission risk predictors can improve the predictive power and provide stronger risk adjustments for the commercially insured younger population than the models currently in use. A simulation of the penalty policy illustrated that more refined adjustment methods reduce penalties applied to teaching hospitals, hospitals in a system with more than three hospitals, and large hospitals. Using an economic framework, this dissertation also explains the Health Management Organization (HMO) insurance effect on hospital length of stay and 30-day readmission rates. Results from multivariate analyses indicate that on average HMO patients had shorter length of stay than Preferred Provider Organization (PPO) patients and that on average HMO patients had higher readmission rates. The HMO insurance effects show heterogeneity among various geographical regions, hospital types and patient age groups. This dissertation offers insights to policy makers on reforming healthcare payment systems while improving health care quality.

Table of Contents

  • Chapter One

    Introduction

  • Chapter Two

    30-Day Readmission Rates of Commercially Insured Under 65 Adult Patients in California

  • Chapter Three

    Risk Adjustment Methods

  • Chapter Four

    Risk Adjustment Methods in Hospital Comparison and Sensitivity Analyses

  • Chapter Five

    Readmission Rates and Hospital Length of Stay: HMO vs. PPO

  • Chapter Six

    Discussion, Conclusions and Policy Implications

  • Appendix One

    Population Characteristics and Primary Diagnosed Condition Group Classification

  • Appendix Two

    List and Frequency of Primary Diagnoses by AHRQ Single Level Diagnosis CCS Code

  • Appendix Three

    30-Day Risk Adjustment Model Formulae

  • Appendix Four

    Logistic Model Results from the Preferred Model

  • Appendix Five

    Model Specification and Estimation for the Length of Stay Analysis Using the Hurdled Model

  • Appendix Six

    Model Specification and Estimation for the Length of Stay Analysis Using the Two-Part OLS based with log transformation on the dependent variable

  • Appendix Seven

    Results of the Sensitivity Analyses in Chapter 5

Research conducted by

This document was submitted as a dissertation in December 2015 in partial fulfillment of the requirements of the doctoral degree in public policy analysis at the Pardee RAND Graduate School. The faculty committee that supervised and approved the dissertation consisted of Glenn Melnick (Chair), Emmett Keeler, and Kathleen Mullen.

This report is part of the RAND Corporation dissertation series. Pardee RAND dissertations are produced by graduate fellows of the Pardee RAND Graduate School, the world's leading producer of Ph.D.'s in policy analysis. The dissertations are supervised, reviewed, and approved by a Pardee RAND faculty committee overseeing each dissertation.

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