From Data to Improved Decisions: Operations Research in Healthcare Delivery

被引：19

作者：

Capan, Muge ^{[1
]}

Khojandi, Anahita ^{[2
]}

Denton, Brian T. ^{[3
]}

Williams, Kimberly D. ^{[1
]}

Ayer, Turgay ^{[4
]}

Chhatwal, Jagpreet ^{[5
,6
]}

Kurt, Murat ^{[7
]}

Lobo, Jennifer Mason ^{[8
]}

Roberts, Mark S. ^{[9
]}

Zaric, Greg ^{[10
]}

Zhang, Shengfan ^{[11
]}

Schwartz, J. Sanford ^{[12
]}

机构：

[1] Christiana Care Hlth Syst, Value Inst, John H Ammon Med Educ Ctr, 4755 Ogletown Stanton Rd, Newark, DE 19718 USA

[2] Univ Tennessee, Dept Ind & Syst Engn, Knoxville, TN USA

[3] Univ Michigan, Ind & Operat Engn & Urol, Ann Arbor, MI 48109 USA

[4] Georgia Inst Technol, H Milton Stewart Sch Ind & Syst Engn, Ctr Hlth & Humanitarian Syst, Atlanta, GA 30332 USA

[5] Harvard Univ, Harvard Med Sch, Inst Technol Assessment, Boston, MA 02115 USA

[6] Massachusetts Gen Hosp, Boston, MA 02114 USA

[7] Merck Res, Whitehouse Stn, NJ USA

[8] Univ Virginia, Dept Publ Hlth Sci, Charlottesville, VA USA

[9] Univ Pittsburgh, Grad Sch Publ Hlth, Dept Hlth Policy & Management, Pittsburgh, PA USA

[10] Univ Western Ontario, Richard Ivey Sch Business, London, ON, Canada

[11] Univ Arkansas, Dept Ind Engn, Fayetteville, AR 72701 USA

[12] Univ Penn, Perelman Sch Med, Div Gen Internal Med, Philadelphia, PA 19104 USA

来源：

MEDICAL DECISION MAKING | 2017年 / 37卷 / 08期

关键词：

Operations research; health systems optimization; data-driven modeling; analytics; evidence-based solutions; PROSTATE-CANCER; MODEL; TRANSPLANTATION;

D O I：

10.1177/0272989X17705636

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

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.

引用

页码：849 / 859

页数：11

共 56 条

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