A continuous-time Markov model for estimating readmission risk for hospital inpatients

Abstract

Research concerning hospital readmissions has mostly focused on statistical and machine learning models that attempt to predict this unfortunate outcome for individual patients. These models are useful in certain settings, but their performance in many cases is insufficient for implementation in practice, and the dynamics of how readmission risk changes over time is often ignored. Our objective is to develop a model for aggregated readmission risk over time – using a continuous-time Markov chain – beginning at the point of discharge. We derive point and interval estimators for readmission risk, and find the asymptotic distributions for these probabilities. Finally, we validate our derived estimators using simulation, and apply our methods to estimate readmission risk over time using discharge and readmission data for surgical patients.

Keywords:

• Hospital readmission
• continuous-time Markov chain
• asymptotic distribution
• stochastic process
• survival analysis

2010 Mathematics Subject Classification:

• 62M05

Acknowledgments

The authors are thankful to those who have provided hospital readmission data, including David Anderson at Villanova University and his collaborators at the University of Maryland Medical Center.

Disclosure statement

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.