Soroush Saghafian Photo

Soroush Saghafian

Appointment
Assistant Professor of Public Policy
Office Address
79 John F. Kennedy St. Littauer Bldg 205
617-496-1748
Kilinc, Derya, Soroush Saghafian, and Stephen Traub. "Dynamic Assignment of Patients to Primary and Secondary Inpatient Units: Is Patience a Virtue?" HKS Faculty Research Working Paper Series RWP17-010, December 2016.

Abstract

An important contributor to the well-known problem of Emergency Department (ED) overcrowding is prolonged boarding of patients who are admitted through the ED. Patients admitted through the ED constitute about 50% of all non-obstetrical hospital admissions, and may be boarded in the ED for long hours with the hope of finding an available bed in their primary inpatient unit. We study effective ways of reducing ED boarding times by considering the trade-off between keeping patients in the ED and assigning them to a secondary inpatient unit. The former can increase the risk of adverse events and cause congestion in the ED, whereas the latter may adversely impact the quality of care. Further complicating this calculus is the fact that a secondary inpatient unit for a current patient can be the primary unit for a future arriving patient; assignments, therefore, should be made in an orchestrated way. Developing a queueing-based Markov decision process, we first demonstrate that patience in transferring patients is a virtue, but only up to a point. We also find that, contrary to the prevalent perception, idling inpatient beds can be beneficial. Since the optimal policy for dynamically assigning patients to their primary and secondary inpatient units is complex and hard to implement in hospitals, we develop a simple policy which we term penalty-adjusted Largest Expected Workload Cost (LEWC-p). Using simulation analyses calibrated with hospital data, we find that implementing this policy could significantly help hospitals to improve their patient safety by reducing boarding times while controlling the overflow of patients to secondary units. Using both data analyses and various simulation experiments, we also help managers by generating insights into hospital conditions under which achievable improvements are significant.