Search

Objective: To establish a case-adjusted hospital-specific performance evaluation tool using machine learning methodology for cesarean delivery. Data Sources: Secondary data were collected from patients between January 1, 2015 and February 28, 2018 using a hospital's 'Electronic Data Warehouse' database from Illinois, USA. Study Design: The machine learning methodology of optimal classification trees (OCTs) was used to predict cesarean delivery rate by physician group, thereby establishing the case-adjusted benchmarking standards in comparison to the overall hospital cesarean delivery rate. Outcomes of specific patient populations of each participating practice were predicted, as if each were treated in the overall hospital environment. The resulting OCTs estimate physician group expected cesarean delivery outcomes, both aggregate and in specific clinical situations. Data Collection/Extraction Methods: Twelve thousand eight hunderd and forty one singleton, vertex, term deliveries, cared for by practices with [greater than or equal to]50 births. Principal Findings: The overall rate of cesarean delivery was 18.6% (n = 2384), with a range of 13.3%-33.7% amongst 22 physician practices. An optimal decision tree was used to create a prediction model for the hospital overall, which defined 23 patient cohorts divided by 46 nodes. The model's performance for prediction of cesarean delivery is as follows: area under the curve 0.73, sensitivity 98.4%, specificity 16.1%, positive predictive value 83.7%, negative predictive value 70.6%. Comparisons with the overall hospital's specific-case adjusted benchmark groups revealed that several groups outperformed the overall hospital benchmark, and some practice groups underperformed in comparison to the overall hospital benchmark. Conclusions: OCT benchmarking can assess physician practice-specific case-adjusted performance, both overall and clinical situation-specific, and can serve as a valuable tool for hospital self-assessment and quality improvement. KEYWORDS cesarean birth, cesarean delivery, cesarean section, database, machine learning, optimal classification trees, risk analysis/modeling, statistics What is known on this topic * The cesarean delivery rate in the United States for low-risk births is higher than the World Health Organization's target rate and associated with increased maternal morbidity and mortality. * Criticisms of prior benchmarking models are the inclusion of limited patient-related factors, reporting of a single summary measure of hospital performance, and outdated analytical methods to predict risk based only on binary risk factors. * Crude comparison of cesarean delivery rates between hospitals or practice groups may not be appropriate given variation in case mix. What this study adds * Optimal classification trees (OCTs) benchmarking analysis is a novel and nonlinear technology that can assess physician practice-specific case-adjusted performance for cesarean delivery with high sensitivity of 98.4% in this study. * OCT benchmarking analysis can be used to compare practice group performance in cesarean delivery with an area under the curve of 0.73. * OCT benchmarking analysis can compare practice groups and reveal differences in performance in comparison to the overall hospital's performance., 1 | INTRODUCTION The current cesarean delivery (CD) rate in the United States for the most recent data from 2018 was 31.9%, with a low-risk CD rate (nulliparous, term, singleton, [...]