Exploring the Value of Waiting During Labor

Karen Hicklin (), Julie S. Ivy (), Fay Cobb Payton (), Meera Viswanathan () and Evan Myers ()
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Karen Hicklin: Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
Julie S. Ivy: Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina 27595
Fay Cobb Payton: School of Management, North Carolina State University, Raleigh, North Carolina 27595
Meera Viswanathan: RTI International, Research Triangle Park, North Carolina 27709;
Evan Myers: School of Medicine, Obstetrics and Gynecology, Duke University, Durham, North Carolina 27710

Service Science, 2018, vol. 10, issue 3, 334-353

Abstract: Of the nearly four million births that occur each year in the United States, almost one in three is a cesarean delivery. Despite the increasing C-section rate over the years, there is no evidence that the increase has caused a decrease in neonatal or maternal mortality or morbidity. Bayesian decision analysis is used to model the decision between classifying a patient as “failure-to-progress,” which is cause for a C-section, using current information (prior probability) or information gathered (posterior probability) as labor continues. The Bayesian decision models determine the conditions under which it is appropriate to gather additional information (i.e., take an observation) before deciding to end labor and perform a C-section based on the decision maker’s belief about successful labor. During an observation period, the decision maker learns more about the patient and her medical state and the likelihood of a successful vaginal delivery is updated. This study determines the conditional value of information (conditional on the decision maker’s prior belief) and determines the conditions under which information has positive value. This model can be used to facilitate shared decision making for labor and delivery through communicating beliefs, risk perceptions, and the associated actions. The online appendix is available at https://doi.org/10.1287/serv.2018.0205 .

Keywords: Bayesian decision analysis; value of information; mode of delivery; health services and networks; medical decision making (search for similar items in EconPapers)
Date: 2018
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