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Practice Variation, Bias, and Experiential Learning in Cesarean Delivery: A Data-Based System Dynamics Approach

Authors

  • Navid Ghaffarzadegan Ph.D., M.B.A.,

    Corresponding author
    1. Engineering Systems Division, Massachusetts Institute of Technology, Boston, MA
    • Address correspondence to Navid Ghaffarzadegan, Ph.D., M.B.A., Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, MA; e-mail: navidg@mit.edu.

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  • Andrew J. Epstein Ph.D., M.P.P.,

    1. Philadelphia Veterans Affairs Medical Center & Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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  • Erika G. Martin Ph.D., M.P.H.

    1. Rockefeller College of Public Affairs and Policy and Nelson A. Rockefeller Institute of Government, University at Albany, State University of New York, Albany, NY
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Abstract

Objectives

To simulate physician-driven dynamics of delivery mode decisions (scheduled cesarean delivery [CD] vs. vaginal delivery [VD] vs. unplanned CD after labor), and to evaluate a behavioral theory of how experiential learning leads to emerging bias toward more CD and practice variation across obstetricians.

Data Sources/Study Setting

Hospital discharge data on deliveries performed by 300 randomly selected obstetricians in Florida who finished obstetrics residency and started practice after 1991.

Study Design

We develop a system dynamics simulation model of obstetricians' delivery mode decision based on the literature of experiential learning. We calibrate the model and investigate the extent to which the model replicates the data.

Principal Findings

Our learning-based simulation model replicates the empirical data, showing that physicians are more likely to schedule CD as they practice longer. Variation in CD rates is related to the way that physicians learn from outcomes of past decisions and accumulate experience.

Conclusions

The repetitive nature of medical decision making, learning from past practice, and accumulating experience can account for increases in CD decisions and practice variation across physicians. Policies aimed at improving medical decision making should account for providers' feedback-based learning mechanisms.

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