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
30-Day Readmission Rates of Commercially Insured Under 65 Adult Patients in California
Risk Adjustment Methods
Risk Adjustment Methods in Hospital Comparison and Sensitivity Analyses
Readmission Rates and Hospital Length of Stay: HMO vs. PPO
Discussion, Conclusions and Policy Implications
Population Characteristics and Primary Diagnosed Condition Group Classification
List and Frequency of Primary Diagnoses by AHRQ Single Level Diagnosis CCS Code
30-Day Risk Adjustment Model Formulae
Logistic Model Results from the Preferred Model
Model Specification and Estimation for the Length of Stay Analysis Using the Hurdled Model
Model Specification and Estimation for the Length of Stay Analysis Using the Two-Part OLS based with log transformation on the dependent variable
Results of the Sensitivity Analyses in Chapter 5