Identification of readmission risk factors by analyzing the hospital-related state transitions of congestive heart failure (CHF) patients

The hospital length-of-stay (LOS), and the time between a discharge and the next admission, are important measures of healthcare utilization, and are generally positively skewed. We model the state transitions of CHF patients, using data from the Veterans Health Administration (VHA), by fitting a Coxian phase-type distribution to their LOS data, and extract the associated states in the latent Markov process. Selecting an appropriate number of phases helps to account for some heterogeneity among different LOS groups within the hospital, and provides a way to interpret each added covariate. By analyzing the strength of the connections among patient social, clinical, and historical characteristics within each group, the associated readmission risk may be estimated. For example, we found that groups with a greater LOS tended to have a greater proportion of patients from nursing home care. Nursing home care patients, who belong to the greater LOS group, tended to have a decreased readmission risk. Thus, by increasing the LOS of CHF patients whose characteristics lead to their inclusion into a nursing home group, or who enter the hospital from a nursing home, we might be able to reduce their risk of readmission.