Muge Capan1, Anahita Khojandi2, Brian T Denton3, Kimberly D Williams1, Turgay Ayer1,4, Jagpreet Chhatwal5, Murat Kurt6, Jennifer Mason Lobo7, Mark S Roberts8, Greg Zaric9, Shengfan Zhang10, J Sanford Schwartz11. 1. Christiana Care Health System, Value Institute, John H. Ammon Medical Education Center, Newark, DE, USA (MC, KDW). 2. Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN, USA (AK). 3. Industrial and Operations Engineering and Urology, University of Michigan, Ann Arbor, MI, USA (BTD). 4. Georgia Institute of Technology H Milton Stewart School of Industrial and Systems Engineering, Center for Health & Humanitarian Systems, Atlanta, GA, USA (TA). 5. Harvard University, Harvard Medical School, Institute for Technology Assessment; Massachusetts General Hospital, Boston, MA, USA (JC). 6. Merck Research, Whitehouse Station, NJ, USA (MK). 7. Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA (JML). 8. Department of Health Policy and Management, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA (MSR). 9. Richard Ivey School of Business University of Western Ontario, London, ON, Canada (GZ). 10. Department of Industrial Engineering, University of Arkansas, Fayetteville, AR, USA (SZ). 11. General Internal Medicine Division, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA (JSS).
Abstract
BACKGROUND: The Operations Research Interest Group (ORIG) within the Society of Medical Decision Making (SMDM) is a multidisciplinary interest group of professionals that specializes in taking an analytical approach to medical decision making and healthcare delivery. ORIG is interested in leveraging mathematical methods associated with the field of Operations Research (OR) to obtain data-driven solutions to complex healthcare problems and encourage collaborations across disciplines. This paper introduces OR for the non-expert and draws attention to opportunities where OR can be utilized to facilitate solutions to healthcare problems. METHODS: Decision making is the process of choosing between possible solutions to a problem with respect to certain metrics. OR concepts can help systematically improve decision making through efficient modeling techniques while accounting for relevant constraints. Depending on the problem, methods that are part of OR (e.g., linear programming, Markov Decision Processes) or methods that are derived from related fields (e.g., regression from statistics) can be incorporated into the solution approach. This paper highlights the characteristics of different OR methods that have been applied to healthcare decision making and provides examples of emerging research opportunities. EXAMPLES: We illustrate OR applications in healthcare using previous studies, including diagnosis and treatment of diseases, organ transplants, and patient flow decisions. Further, we provide a selection of emerging areas for utilizing OR. CONCLUSIONS: There is a timely need to inform practitioners and policy makers of the benefits of using OR techniques in solving healthcare problems. OR methods can support the development of sustainable long-term solutions across disease management, service delivery, and health policies by optimizing the performance of system elements and analyzing their interaction while considering relevant constraints.
BACKGROUND: The Operations Research Interest Group (ORIG) within the Society of Medical Decision Making (SMDM) is a multidisciplinary interest group of professionals that specializes in taking an analytical approach to medical decision making and healthcare delivery. ORIG is interested in leveraging mathematical methods associated with the field of Operations Research (OR) to obtain data-driven solutions to complex healthcare problems and encourage collaborations across disciplines. This paper introduces OR for the non-expert and draws attention to opportunities where OR can be utilized to facilitate solutions to healthcare problems. METHODS: Decision making is the process of choosing between possible solutions to a problem with respect to certain metrics. OR concepts can help systematically improve decision making through efficient modeling techniques while accounting for relevant constraints. Depending on the problem, methods that are part of OR (e.g., linear programming, Markov Decision Processes) or methods that are derived from related fields (e.g., regression from statistics) can be incorporated into the solution approach. This paper highlights the characteristics of different OR methods that have been applied to healthcare decision making and provides examples of emerging research opportunities. EXAMPLES: We illustrate OR applications in healthcare using previous studies, including diagnosis and treatment of diseases, organ transplants, and patient flow decisions. Further, we provide a selection of emerging areas for utilizing OR. CONCLUSIONS: There is a timely need to inform practitioners and policy makers of the benefits of using OR techniques in solving healthcare problems. OR methods can support the development of sustainable long-term solutions across disease management, service delivery, and health policies by optimizing the performance of system elements and analyzing their interaction while considering relevant constraints.
Entities:
Keywords:
Operations research; analytics; data-driven modeling; evidence-based solutions; health systems optimization
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