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. 2023 Mar 31;17(1):6.
doi: 10.1186/s13037-023-00356-x.

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

Dana Arad  1   2 , Ariel Rosenfeld  3 , Racheli Magnezi  4
Affiliations

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

Dana Arad et al. Patient Saf Surg. .

Abstract

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.

Keywords: Machine learning; Never event; Patient safety; Surgery department.

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Conflict of interest statement

To the best of our knowledge, the named authors have no competing interests, financial or otherwise to disclose.

This study was funded by grant #MOHIG 14-2019 from the Medical Research Fund for Health Services–Jerusalem.

D.A. performed the data collection and analyzed the observations and root cause analyses for the dataset and possible contributing factors. A.R. interpreted and created the algorithms for machine learning analysis, and R.M. made a major contribution to the writing of the manuscript. All authors read and approved the final manuscript.

Figures

Fig. 1
Fig. 1
Top 15 contributing features for the six examined departments
Fig. 2
Fig. 2
Effect of two features’ combination on prediction by surgical departments
Fig. 3
Fig. 3
Features affecting the wrong surgery site (type A)
Fig. 4
Fig. 4
Features affecting retained foreign items during surgery (type B)
See this image and copyright information in PMC

References

    1. Kjellberg J, Wolf RT, Kruse M, Rasmussen SR, Vestergaard J, Nielsen KJ, Rasmussen K. Costs associated with adverse events among acute patients. BMC Health Serv Res. 2017;17(1):1–7. doi: 10.1186/s12913-017-2605-5. - DOI - PMC - PubMed
    1. Robert MC, Choi CJ, Shapiro FE, Urman RD, Melki S. Avoidance of serious medical errors in refractive surgery using a custom preoperative checklist. J Cataract Refract Surg. 2015;41(10):2171–8. doi: 10.1016/j.jcrs.2015.10.060. - DOI - PubMed
    1. Provisional publication of Never Events reported as occurring between 1 February and 31. March 2018. London, England: National Health Service, April 27, 2018. (https://improvement.nhs.uk/documents/2613/Never_Events_1_February_to_31_...)
    1. Provisional publication. of Never Events reported as occurring between 1 April
    1. 2018. and 31 January 2019. London, England: National Health Service, February 27, 2019. (https://improvement.nhs.uk/documents/4872/Provisional_publication_-__NE_...)

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