Publication Type

PhD Dissertation

Publication Date



The emergency room (ER) – or emergency department (ED) – is often seen as a place with long waiting times and a lack of doctors to serve the patients. However, it is one of the most important departments in a hospital, and must efficiently serve patients with critical medical needs. In the existing literature, addressing the issue of long waiting times in an ED often takes the form of single-faceted queue-management strategies that are either from a demand perspective or from a supply perspective. From the demand perspective, there is work on queue design such as priority queues, or queue control strategies such as a fast-track system and demand restriction through ambulance diversion. On the supply side, existing studies looked at the management of the supply of resources (e.g., doctors, nurses, equipment). However, they may not sufficiently leverage insights that can be derived from both historical and real-time data. In this dissertation, we present an integrated framework that manages queues dynamically in the ED from both the demand and supply perspectives by leveraging historical data and real-time data. More precisely, we introduce data-driven and intelligent dynamic patient-prioritization strategies to manage the demand concurrently with dynamic resource-adjustment policies to man- age supply. Our framework allows decision-makers to select both demand-side and supply-side strategies to suit the needs of their ED. We verify through simulation that strategies from both perspectives work well together in our proposed framework. The results show that such a framework improves aver- age patient length-of-stay (LOS) in the ED without having to restrict demand (stop patients from coming to the ED). In our dynamic patient-prioritization strategies, we propose and evaluate three schemes to allocate patients to doctors: shortest-consultation-time-first (SCON), shortest-remaining-time-first (SREM) and a mixed strategy (MIXED). We test the strategies using simulation and our experimental results show that a dynamic priority queue is effective in reducing the LOS of patients and hence improving patient flow. This is found to be better than standard queuing solu- tions which are based on first-in-first-out (FIFO) or static priority queues. We present results that show a trade-off between performance and risks (in terms of implementation complexity, and starvation, a situation where a patient is deprived of the chance to consult a doctor). We show that decision-makers in healthcare institutions can use the information to choose a strategy that is most suitable for their ED. On the supply side, we consider the problem of allocating doctors in the ambulatory area of the ED based on a set of policies. Traditional staffing policies are static and do not react well to surges in patient demand. By leveraging real-time and historical information, we provide strategies in two dimensions: (1) the ability to react to changes in demand and (2) to optimize the doctor schedule so as to satisfy the hospital’s desired service quality in terms of LOS. Our main contribution is a data-driven approach that performs online real- location of doctor resources through symbiotic simulation in real time using historical as well as current arrival rates. We build a simulation prototype to demonstrate that this can be done. The experimental results from our prototype show that our approach allows the hospital to cope with varying levels of demand and to better serve the patients within the desired service level. In addition, the prototype offers insights into the trade-off between performance and risk (in terms of implementation complexity and doctor schedule stability). As such, we provide analysis and opportunities for decision-makers to select a strategy which fits the hospital concerned.


intelligence systems, healthcare operation improvement, decision analytics, process improvement, emergency department, dynamic queues

Degree Awarded

PhD in Information Systems


Computer Sciences | Health and Medical Administration | Operations Research, Systems Engineering and Industrial Engineering


LAU, Hoong Chuin

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Singapore Management University

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Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.