Managing a patient waiting list with time-dependent priority and adverse events

Abstract

This paper addresses the problem of managing a waiting list for elective surgery to decide the number of patients selected from the waiting list and to schedule them in accordance with the operating room capacity in the next period. The waiting list prioritizes patients not only by their initial urgency level but also by their waiting time. Selecting elective surgery patients requires a balance between the waiting time for urgent patients and that for less urgent patients. The problem is formulated as an infinite horizon Markov Decision Process. Further, the study proposes a scheduling procedure based on structural properties of an optimal policy by taking a sampling-based finite horizon approximation approach. Finally, we examine the performance of the policy under various conditions.Article published by EDP Sciences c EDP Sciences, ROADEF, SMAI 2013Theorem 4.1. If v N +1 (s) = 0 for all s, then, J n (s, a) is jointly convex in s and u and v n (s) is convex in s for all n = 1, 2, . . . , N.Proof. The proof is given by induction on n.Here v N +1 (s) and J N (s, u) are convex by assumption and Proposition 4.3. Assuming that the results hold for n, v n (s) and J n (s, u) are convex. We now show that the results hold also for n − 1. Because both v n (s) and c (s, u) are convex, J n−1 (s, u) is also convex. The convexity of J n−1 (s, u) supports that v n−1 (s) is convex.

An approximate dynamic programming approach to the admission control of elective patients

“…Hospital data show that this pre-admission model allows homogeneous and standardized prioritization and enhances transparency, efficiency and equity. Min and Yih (2014) conduct numerical simulations that compare the effects of static and time-dependent dynamic prioritization on patients' waiting time. The results indicate that the waiting time for non-urgent patients is much longer than that of urgent ones when the static priority is applied, whereas the dynamic priority minimizes the total weighted waiting time of all patients, so that the gap in the length of waiting time between urgent patients and non-urgent ones is significantly reduced.…”

An integrated approach to demand and capacity planning in outpatient clinics

“…In allocation scheduling, "a wait list is maintained and patients are notified on the day of their appointments", whereas in advance scheduling, "patients are given appointments in the future at the time of request" (Truong 2015, p. 1). See Gerchak et al (1996), Ayvaz and Huh (2010), and Min and Yih (2014) for allocation scheduling, and Patrick et al (2008) and Gocgun and Ghate (2012) for advance scheduling.…”

Simulation Model to Study Provider Capacity Release Schedules under Time-Varying Demand Rate for Acute Appointments, Demand for Follow-Up Appointments, and Time-Dependent No Show Rate

“…In the operations management literature, outpatient appointment scheduling (OAS) has been examined through a number of lenses relating to measures of the objective, the time horizon, as well as modeling and solution methodologies [10,11]. For example, Min and Yih used an infinite horizon MDP model to study the problem of managing a waiting list for elective surgery [12]. Gocgun and Puterman [13] formulated as an MDP the problem of scheduling patients for chemotherapy sessions which required appointments at specific future days within a treatment specific time window.…”