Optimizing Patient Scheduling and Capacity Management to Provide Timely Access to Care

Abstract

In this thesis, we explore diverse challenges in healthcare resource allocation and patient scheduling to optimize efficiency across various healthcare settings. The research delves into three distinct yet interconnected problems: elective patient admission scheduling during pandemics, scheduling and routing of home care services with periodic visits and time windows, and patient appointment scheduling in clinic settings. By proposing adaptive scheduling policies, advanced algorithms, and simulation optimization approaches, we aim to enhance healthcare systems' effectiveness and resource utilization. The findings from these chapters offer valuable insights for healthcare providers, giving innovative solutions to improve patient care and healthcare services. In Chapter 2, we discuss patient admission scheduling under pandemic conditions. This chapter was inspired by the challenges faced by hospitals during the COVID-19 pandemic. Over the pandemic, thousands of hospital elective admissions worldwide were postponed. This led to significant idle capacity. In this chapter, we present an adaptive elective admission scheduling policy that allows the admittance of more elective patients in hospitals during a pandemic while ensuring that hospitals can vacate a certain number of beds as needed over a short warning period. We propose an iterative heuristic approach that schedules elective patients in a manner that maintains a target elective patient admission rate, while also achieving "nimbleness" - a property we define as the ability to reduce the hospital's census to a specified target within a short warning period (i.e., 5 days) and with a given level of confidence. In Chapter 3, we investigate a scheduling and routing problem for personal support workers (PSWs) in a dynamic environment. Our problem involves scheduling and routing periodic visits to clients by personal support workers while taking into account several constraints, including clients' visit time preferences, PSWs' working hour limits, and breaks between and within shifts. Our aim was to minimize travel times, maximize continuity of care for clients, minimize the number of outsourced PSWs (PSWs from other agencies), and minimize the violation of clients' preferred visit times. To address this strongly NP-hard and multi-objective combinatorial problem, we propose a modified version of the nondominated sorting genetic algorithm II (NSGA-II). We introduce novel route-creator procedures that provide more exploration and diversity in the solution space. To evaluate the performance of our solution approach, we test it using data from a personal support provider company over a two-week period. The results demonstrate a reduction in the average number of PSWs assigned to a client, a decrease in the average idle time and travel time of PSWs, and a significant reduction in the number of visits outsourced to other agencies. Overall, our proposed algorithm shows promising results in terms of efficiency and effectiveness; creating a scheduling and routing plan for complex scheduling and routing problems. Chapter 4 addresses the problem of patient appointment scheduling in a clinic setting where patients utilize multiple resources. The goal is to provide a scheduling template to minimize patient wait times, clinic overtime, and deviations from historical proportions of patients with different treatments, while maximizing patient throughput and resource utilization. To achieve this, a simulation optimization approach, a metaheuristic, and three heuristic approaches were implemented and tested using historical data from a children's hospital plaster clinic in Ontario, Canada. The results of the study revealed that the different approaches excelled in different objectives while all improved on the existing scheduling practice. The resulting templates notably decrease both wait times and clinic overtime, concurrently improving patient throughput and optimizing resource utilization. This approach serves as a valuable tool for healthcare managers, empowering them to enhance clinic efficiency and patient satisfaction.