Wednesday, September 25, 2019 - 1:30pm to 2:30pm
ISyE Groseclose Room 402
Timing it Right: Balancing Inpatient Congestion versus Readmission Risk at Discharge
When to discharge a patient plays an important role in hospital patient flow management and patient outcomes. In this work, we develop and implement a practical decision support tool to aid hospitals in managing the delicate balance between readmission risk at discharge and ward congestion. We formulate the discharge decision framework as a large-scale Markov Decision Process (MDP) that integrates a personalized readmission prediction model to dynamically prescribe both how many and which patients to discharge each day. We overcome challenges from both the analytical and prediction sides. Due to patient heterogeneity and the fact that length-of-stay is not memoryless, the MDP suffers the curse of dimensionality. We derive useful structural properties and leverage an analytical solution for a special cost setting to transform the MDP into a univariate optimization; this leads to an efficient dynamic heuristic. Meanwhile, off-the-shelf prediction models alone could not provide adequate input for our decision support framework. To bridge this gap, we integrate several statistical methods to build a new readmission prediction model that allows us to implement our decision framework with existing hospital data systems.
Through extensive counterfactual analyses, we demonstrate the value of our recommended discharge policy over our partner hospital’s historical discharge behavior. We also discuss the implementation efforts of this discharge optimization tool at our partner hospital. This is joint work with Jonathan Helm at Kelley School of Business at Indiana University and industry partners. The paper received the Pierskalla Best Paper Award at INFORMS 2018 and can be accessed at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3202975
Pengyi Shi joined the Krannert School of Management at Purdue University as an Assistant Professor in January 2014. She received her Ph.D. degree in Industrial Engineering from Georgia Institute of Technology before joining Purdue. Her research interests include healthcare operations and stochastic modeling, with a focus on hospital patient flow management. She has collaborated with practitioners and faculty members from different healthcare organizations, including major hospitals in the US, Singapore, and China.