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. .