Optimal use of operating room (OR) personnel and resources is an important problem from multiple perspectives. Unanticipated waits and delays continue to add tremendous costs and negatively impact outcomes. There are over 55 million ambulatory and 45 million inpatient procedures performed in the U.S. annually, with increasing case complexities and patient comorbidities. Operating suites (the group of ORs at a facility), account for 40-70% of hospital revenue. The average surgical case starts on time just 27 percent of the time, the average turnover time is over twice the best practice, and the average OR runs at only 68 percent capacity. Improving throughput by just one additional procedure per day per OR suite should increase annual revenue for the average-sized organization by $4M to $7M. Thus, there is great opportunity for improved utilization of these scarce resources through enhancing OR efficiency, as well as fiscal and tangible value to be realized in improved morbidity, mortality, quality of life, and patient satisfaction. Several methods have been applied attempting to optimize surgical scheduling, including mathematical programming, simulation, and improvement heuristics. However, little is known regarding the actual performance of such tools in practice. In particular, there is no system currently available to optimally manage surgical procedures that account for contextual challenges like constrained resources. There is a critical need for novel solutions that consider the multiple factors and systems shown to influence OR performance. We have assembled an outstanding interdisciplinary team to effectively address this complex problem. We propose the measurement and analysis of multiple levels of workflow data, in order to inform development of a suite of information decision support tools based on mathematical algorithms used in advanced manufacturing. These tools will lead to markedly improved scheduling accuracy, timely updates of system resources, fewer system delays, improved resource utilization, and superior patient and financial outcomes. We propose a phase one NIH STTR project with the following specific aims and methods: Aim 1 - Develop the business case and return on investment (ROI) model for optimizing OR efficiency; using i) interviews of key informants; and ii) an ROI analysis of revenue gained vs. implementation cost. Aim 2 - Define the task flow network and determine data required to model OR efficiency; through: i) identifying essential data for model development in existing OR information systems and through observational studies; and ii) develop a mathematical model that displays OR workflow validated through expert review. Aim 3 - Demonstrate the technical feasibility of the model solution and analysis for tactical purposes; by: i) extending APC's VirtECS Scheduling engine for the OR efficiency optimization model; ii) perform scenario analyses, and iii) compare model solutions to actual efficiency measures and validate results.
Public Health Relevance: The ultimate goal of this project is to develop a software package and workflow process to optimize the utilization and scheduling of patients, operating rooms, staffing, technology, supplies and hospital beds. This package will provide powerful capabilities for strategic resource planning, tactical scheduling, and scenario analysis which will reduce patient wait times and increase hospital revenue. The aim of this Phase I project is to investigate the technical and financial feasibility of such a system, based on the VirtECS Scheduling engine of Advanced Process Combinatorics, Inc.
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