Add like
Add dislike
Add to saved papers

Factors contributing to preventing operating room "never events": a machine learning analysis.

BACKGROUND: A surgical "Never Event" is a preventable error occurring immediately before, during or immediately following surgery. Various factors contribute to the occurrence of major Never Events, but little is known about their quantified risk in relation to a surgery's characteristics. Our study uses machine learning to reveal and quantify risk factors with the goal of improving patient safety and quality of care.

METHODS: We used data from 9,234 observations on safety standards and 101 root-cause analyses from actual, major "Never Events" including wrong site surgery and retained foreign item, and three random forest supervised machine learning models to identify risk factors. Using a standard 10-cross validation technique, we evaluated the models' metrics, measuring their impact on the occurrence of the two types of Never Events through Gini impurity.

RESULTS: We identified 24 contributing factors in six surgical departments: two had an impact of > 900% in Urology, Orthopedics, and General Surgery; six had an impact of 0-900% in Gynecology, Urology, and Cardiology; and 17 had an impact of < 0%. Combining factors revealed 15-20 pairs with an increased probability in five departments: Gynecology, 875-1900%; Urology, 1900-2600%; Cardiology, 833-1500%; Orthopedics,1825-4225%; and General Surgery, 2720-13,600%. Five factors affected wrong site surgery's occurrence (-60.96 to 503.92%) and five affected retained foreign body (-74.65 to 151.43%): two nurses (66.26-87.92%), surgery length < 1 h (85.56-122.91%), and surgery length 1-2 h (-60.96 to 85.56%).

CONCLUSIONS: Using machine learning, we could quantify the risk factors' potential impact on wrong site surgeries and retained foreign items in relation to a surgery's characteristics, suggesting that safety standards should be adjusted to surgery's characteristics based on risk assessment in each operating room. .

TRIAL REGISTRATION NUMBER: MOH 032-2019.

Full text links

For the best experience, use the Read mobile app

Group 7SearchHeart failure treatmentPapersTopicsCollectionsEffects of Sodium-Glucose Cotransporter 2 Inhibitors for the Treatment of Patients With Heart Failure Importance: Only 1 class of glucose-lowering agents-sodium-glucose cotransporter 2 (SGLT2) inhibitors-has been reported to decrease the risk of cardiovascular events primarily by reducingSeptember 1, 2017: JAMA CardiologyAssociations of albuminuria in patients with chronic heart failure: findings in the ALiskiren Observation of heart Failure Treatment study.CONCLUSIONS: Increased UACR is common in patients with heart failure, including non-diabetics. Urinary albumin creatininineJul, 2011: European Journal of Heart FailureRandomized Controlled TrialEffects of Liraglutide on Clinical Stability Among Patients With Advanced Heart Failure and Reduced Ejection Fraction: A Randomized Clinical Trial.Review

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

Read by QxMD is copyright © 2021 QxMD Software Inc. All rights reserved. By using this service, you agree to our terms of use and privacy policy.

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app