Optimizing Resource Allocation in Surgery Delivery Systems

Optimization;Simulation;Approximation Methods;Surgery Planning and Scheduling;Surgical Nurse Staffing;Industrial and Operations Engineering;Engineering;Industrial & Operations Engineering

Bam, MayaDaskin, Mark Stephen ;

University of Michigan

关键词: Optimization;    Simulation;    Approximation Methods;    Surgery Planning and Scheduling;    Surgical Nurse Staffing;    Industrial and Operations Engineering;    Engineering;    Industrial & Operations Engineering;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/137041/mbam_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

This thesis focuses on developing mathematical models to optimize processes related to surgery delivery systems. Surgical services account for a large portion of hospital revenue and expenses; moreover, increased demand is expected in the future due in part to the aging population in many countries. Achieving high efficiency in this system is challenging due to the uncertain service durations, the interaction of different stages of the system (e.g., surgery, recovery), and competing criteria (e.g., patient wait time, employee satisfaction, the availability and utilization of healthcare professionals, operating rooms (ORs), and recovery beds). Moreover, solutions must overcome an enormous barrier of computational complexity.Considering the complexity of the problem, and the numerous resources involved in delivering surgical care, this thesis focuses on three aspects of surgery delivery systems: short term scheduling (operational level decisions, e.g., daily sequencing of surgeries), service group team design and staff allocation (strategic level team design decisions on the order of years, and tactical level shift allocation decisions, e.g., monthly), and OR capacity reservation (strategic level decisions, e.g., what OR capacity reservation policy to use in the following years).To optimize scheduling policies on an operational level, we developed a 2-phase approximation method, where the first phase determines the number of ORs to open for the day, and assigns surgeons to ORs. The second phase performs surgical case sequencing considering recovery resource availability. For both phases of the approximation, we provide provable worst-case performance guarantees; furthermore, we use numerical experiments to show the methods also have excellent average case performance. We further developed a mixed integer programming (MIP) model for comparison to the approximation method. We evaluated the performance of the approximation compared to the MIP model in deterministic and stochastic settings, using a discrete even simulation (DES) for the latter.On the strategic and tactical levels, we focus on staffing decisions for surgical nurses. These decisions present a challenge due to nurse availability, skill requirements, hospital regulations, and stochastic surgical demand. We present a MIP to group services into teams, and achieve fairness in training time and overnight surgical volume, and balance size across teams. Once teams are created, we use a MIP-based heuristic to assign shifts to services and teams to ensure coverage of surgical demand. We analyze the performance of the heuristic, and present results that provide insight into optimal surgical nurse staff planning decisions. We show that the newly designed teams are more balanced with respect to the performance metrics, and coverage of surgical demand can be improved.Finally, on the strategic level, we use DES to evaluate OR capacity reservation heuristics. OR capacity reservation is a challenging problem due to uncertain demand for surgery and surgery durations. Using our DES model, we evaluate two categories of approximation methods to gain insights into the problem: first come, first served based heuristics, which are used as benchmarks, and appointment slot reservation heuristics, similar to those used in outpatient clinics. We compare the heuristics based on the mean percent of patients that exceed a predefined surgery access target, mean patient wait time, and mean OR utilization.This research was conducted in collaboration with hospitals, and the problems considered are common to many hospitals. Based on data from these hospitals, we provide evidence that significant improvements could be achieved in the three major decision making levels.