Reducing Hospital Readmissions by Integrating Empirical Prediction with Resource Optimization
Academic Article
Overview
Research
Identity
Additional Document Info
Other
View All
Overview
abstract
Hospital readmissions present an increasingly important challenge for healthcare organizations. Readmissions are expensive and often unnecessary, putting patients at risk and costing $15 billion annually in the United States alone. Currently, 17% of Medicare patients are readmitted to a hospital within 30 days of initial discharge with readmissions typically being more expensive than the original visit to the hospital. Recent legislation penalizes organizations with a high readmission rate. The medical literature conjectures that many readmissions can be avoided or mitigated by postdischarge monitoring. To develop a good monitoring plan it is critical to anticipate the timing of a potential readmission and to effectively monitor the patient for readmission causing conditions based on that knowledge. This research develops new methods to empirically generate an individualized estimate of the time to readmission density function and then uses this density to optimize a postdischarge monitoring schedule and staffing plan to support monitoring needs. Our approach integrates classical prediction models with machine learning and transfer learning to develop an empirical density that is personalized to each patient. We then transform an intractable monitoring plan optimization with stochastic discharges and health state evolution based on delaytime models into a weakly coupled network flow model with tractable subproblems after applying a new pruning method that leverages the problem structure. Using this multimethodologic approach on two large inpatient datasets, we show that optimal readmission prediction and monitoring plans can identify and mitigate 4070% of readmissions before they generate an emergency readmission.
