We analyze the patient flow of three community health clinics from the Seton group in Austin, Texas, using simulation tools. Our goal is to help the clinics find solutions to cope with increasing patient demand. Several scenarios for increasing efficiency are explored using an ARENA-based patient flow model. Multiple bottlenecks are identified and solutions are found to help the clinics minimize overall patient cycle time and to distribute the workload more evenly across the staff. This study demonstrates that healthcare service facilities may benefit from quantitative analysis, especially simulation tools, to improve their efficiency.
In 2015, there were approximately 17,000 General Medicine admissions in the Department of Medicine (DOM) at MGH. General Medicine patients regularly experience significant non-clinical delays caused by bed and care team unavailability, with approximately 25% of patients waiting ten hours or more for a bed. Delays in bed and care team assignments result in decreased patient satisfaction, congestion in the ED and ICUs, and increased overall hospital length-of-stay. This project studies General Medicine patient flow, develops and evaluates interventions to improve this flow, and provides recommendations to hospital leadership. To this end, we construct a discrete-event simulation based on historical data. Intervention effectiveness is measured primarily based on patient-wait-for-bed, the time from when patient is medically ready for an inpatient bed until the bed is assigned to and ready for the patient. We find that the simulation model accurately represents the wait times of General Medicine patients. We propose a new algorithm, which when implemented could reduce overall average patient-wait-for-bed by 9% from 7.36 to 6.67 hours. Implementation of additional capacity and reorganization of the physician care teams (known as the DOM redesign) is shown to result in a further 31% reduction in average wait time (from 6.67 to 4.59 hours). Other interventions tested such as early assignment of patients to care teams based on predicted discharges, and increased flexibility of care teams to cover different units are shown to have modest effects on overall patient-wait-for-bed.
Patient flow is one of the costliest and oft-cited challenges facing hospitals today, and solutions can be expensive or complicated. In light of huge regulatory and financial implications, it is vital that all hospital staff--regardless of their role--learn how they can help to improve patient flow. With hospitals increasingly under the microscope of regulators like the JCAHO and CMS, this video uses sample scenarios to train staff effectively. Small, everyday actions can have a major impact on patient throughput, and this video emphasizes how seemingly minor delays can hinder patient flow and impact patient safety.
Mathematical problems in the intersection of operations research and healthcare operations have received increasing attention in recent years. This thesis is composed of four parts. In the first two parts, we study the surgical scheduling problem via discrete event patient flow simulation, optimization algorithm, and the optimal transport theory. In the last two parts, we briefly introduce atheoretical findings in optimal transport and a real-world application of data-driven decision-making in healthcare operations in response to the outbreak of COVID-19. In the first part, we report the development and deployment of BEDS (better elective day of surgery), a simple algorithm for smoothing across days the number of surgical patients requiring post-procedural beds. We introduce challenges faced by medical institutions for surgical schedule planning and a hospital-level patient flow simulation model, in which the impact of scheduling policies can be modeled without implementation. With the support of simulation results, BEDS was implemented in the Stanford Children's Hospital scheduling system in 2020.7. BEDS does not require significant reductions to surgeon autonomy or centralized scheduling, and is compatible with changes such as surgeon vacations, illness, or joining/leaving the hospital workforce; long-term changes in surgical volumes; and the opening or closing of new operating rooms and post-procedural beds. We present data from over the implementation of BEDS at an academic medical center and make the tool available as a dashboard in Tableau, a commercial software used by hundreds of hospitals in the United States. In the second part, we talk about supply-demand matching and scheduling via optimal transport. The optimal transport problem was formalized by the French mathematician Gaspard Monge in1781. We formulate the offline scheduling problem by a constrained optimization program with a demand density and a capacity bound in the format of optimal transport. We present the results for the optimal plan for the one-dimensionalL1cost function case. Next, we present the results of the optimal plan of the optimal scheduling problem via optimal transport in the presence of class-specific costs. In the third part, we present a result in empirical optimal transport projections with non-symmetric costs. Distributionally Robust Optimization (DRO) is a technique that generates estimators with enhanced generalization performance by introducing an adversary which quantifies the impact in out-of-sample variations in the training set. To provide an optimal approach for choosing the distributional uncertainty size in DRO, we contribute to the literature of DRO by considering more generally optimal transport costs, including the Bregman distance and the local Mahalanobis distance, and present an empirical experiment in portfolio optimization. Finally, we describe a personal protective equipment and hospital policy planning model we developed with Stanford Hospital in response to the outbreak of COVID-19. The model supports early decision-making and policy planning in the hospital to face the global shortage of personal protective equipment.
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