CISE Seminar: Agni Orfanoudaki, Associate Professor, University of Oxford

When Can Machine Learning Help with Operational Efficiency? Designing Vertical Processing Units in Emergency Departments

Addressing hospital emergency department (ED) overcrowding is a critical challenge for many healthcare systems worldwide. Many hospitals (including our partner hospital) have been experimenting with innovative patient flow designs to address this challenge. A promising new design is to separate patients who can be served vertically (e.g., on a regular chair as opposed to horizontally on an ED bed) and route them to a different area termed the Vertical Processing unit, also known as the Rapid Medical Assessment (RMA) unit. Successful implementation of this design significantly depends on understanding which patients and when they should be routed to the RMA unit. To assist our partner hospital, we develop a machine learning model trained on large-scale data capable of providing a personalized risk score for each arriving patient on whether they will eventually need an ED bed.  We then feed these risk scores to an analytical model of patient flow to characterize the optimal protocol for utilizing the RMA unit. Finally, we use simulation analyses calibrated with hospital data to compare the performance of our recommended RMA-based design with more traditional ED flow approaches such as a "fast track" or a "physician in triage" system. Our results suggest that following the RMA design under our recommended protocol can bring several advantages to EDs. It outperforms traditional patient flow designs due to the dynamic and efficient use of ED resources, especially in settings with a higher prevalence of high-acuity patient cases. Overall, this work provides a roadmap to healthcare systems that seek to implement data-driven patient flow systems to improve ED operations.

Agni Orfanoudaki is an Associate Professor of Operations Management at the Saïd Business School of Oxford University. Alongside her role, Agni is a Management Studies Fellow at Exeter College and a visiting scholar at the Harvard Kennedy School as a Harvard Data Science Initiative Fellow. Prior to joining Oxford, Agni received a PhD in Operations Research from the Massachusetts Institute of Technology. Her primary research interests lie at the intersection of optimization and machine learning with applications to healthcare and insurance. Specifically, she has worked on problems related to missing data imputation, survival analysis, clustering, personalized risk prediction, and medical therapy prescription. She has collaborated with numerous institutions, including a major medical society, two international reinsurance companies, and more than eight hospitals in the US and Europe.

Faculty Host: Jinglong Zhao

Student Host: Andres Chavez Armijos