Abstract

This paper provides evidence that in an emergency department (ED), the arrival of an additional low-acuity patient substantially increases the wait time to start of treatment for high-acuity patients. This contradicts a long-standing prior conclusion in the medical literature that this effect is "negligible". The prior methodology underestimates the effect by neglecting how delays are propagated in queueing systems. In contrast, this paper develops and validates a new estimation method based on queueing theory, machine learning, and causal inference. Wait time information displayed to low-acuity patients provide a quasi-randomized instrumental variable, and is used to correct for omitted variable bias. Through a combination of empirical and queueing theoretic analyses, this paper identifies three mechanisms by which a low-acuity patient increases the wait time for high-acuity patients: pre-triage delay, transition delay when an ED interrupts treatment of a low-acuity patient in order to treat a high-acuity patient, and the delay in waiting for test results for high-acuity patients. Thus the paper identifies ways to reduce high-acuity patients' wait time, including: reducing the standard deviation or mean of the transition delay, preventing transition delays by providing vertical or ``fast track" treatment to more low-acuity patients; and designing wait time information systems to divert (especially when the ED is highly congested) low-acuity patients that do not need ED treatment.

Time

12月31日（星期四）9:30-11:00

Speaker

Danqi Luo is a Ph.D. candidate at Stanford University Graduate School of business. Her research applies causal inference and machine learning guided by queueing theory and behavioral operations research insights to operations problems. Her primary research application is in health care, but she is also interested in other service settings, including on-line market places. 

Venue

Zoom会议室ID:99139784980

密码：556789