Exploring the Value of Waiting During Labor

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 .

Introduction to the Special Issue on Advancing Health Services

“…They focus on the impact of risk sensitivity on the optimal health and organ quality thresholds that dictate when a patient should choose to accept and transplant. Hicklin et al (2018) also consider an adaptive acute-care decision-making problem, in particular that of whether to allow a woman in labor to continue or to initiate a Cesarean delivery as a function of cervical dilation and time in labor.…”

“…Like Batun et al (2018) and Hicklin et al (2018), Rojas-Cordova et al (2018) also consider an optimal stopping problem, but in the context of a clinical trial. They examine how the option to terminate the trial early, at one or more points in time for either benefit or futility, impacts drug misclassification rates (i.e., false negatives and false positives).…”