TY - JOUR
T1 - Benchmarking cesarean delivery rates using machine learning-derived optimal classification trees
AU - Gimovsky, Alexis C.
AU - Zhuo, Daisy
AU - Levine, Jordan T.
AU - Dunn, Jack
AU - Amarm, Maxime
AU - Peaceman, Alan M.
N1 - Publisher Copyright:
© 2021 Health Research and Educational Trust.
PY - 2022/8
Y1 - 2022/8
N2 - 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 ≥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.
AB - 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 ≥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.
KW - cesarean birth
KW - cesarean delivery
KW - cesarean section
KW - database
KW - machine learning
KW - optimal classification trees
KW - risk analysis/modeling
KW - statistics
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U2 - 10.1111/1475-6773.13921
DO - 10.1111/1475-6773.13921
M3 - Article
C2 - 34862801
AN - SCOPUS:85122648121
SN - 0017-9124
VL - 57
SP - 796
EP - 805
JO - Health Services Research
JF - Health Services Research
IS - 4
ER -