- Healthcare Operations Management
- Behavioral Operations Management
- Service Operations Management
- Decision Making under Uncertainty
Job Market Paper
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 the two primary mechanisms by which a low-acuity patient increases the wait time for high-acuity patients: pre-triage delay and transition-delay. Thus the paper identifies ways to reduce high-acuity patients’ wait time, including reducing the standard deviation or mean of the transition delay in preemption, 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.
In the field experiments that we are currently running in our main partner hospital, we examine the impact of wait time information provision on patients' waiting experiences, related behaviors, and health outcomes, and how best to communicate such information to benefit patients. To start, we experiment with three different wait time information to low-acuity patients. Through an incentivized survey, patients can electronically self-report their real-time satisfaction on wait times and level of pain throughout their service in the Emergency Department and after. Matching patients' responses with their electronic medical report data and the NRC health data collected by the hospital, we can identify the impact of different wait time information on patients' waiting satisfaction, left without being seen behavior, pain level, and health outcomes.
This paper investigated how the short and long term behavior of the disease invasion can be influenced by heterogeneity. We Looked at heterogeneities in terms of the number of subpopulations and stages of infection. We evaluated quantitative differences in the characteristics of an epidemic in a simple homogeneous population model versus heterogeneous models, and we revealed the conditions under which heterogeneity could be ignored.