The contribution of surgical data science to identifying intraoperative human errors and adverse events in elective liver surgery: A preliminary study

Abstract

Backgrounds/aims: Surgical data science (SDS) is an emerging discipline that aims to enhance the quality of interventional healthcare by capturing and analyzing intraoperative data. Our study focused on identifying human errors (HEs) and adverse events (AEs) during elective liver surgery using an SDS-based approach.

Methods: Intraoperative data from 15 patients undergoing elective open liver resection were collected using an operating room data system (audio, room, and operative field videos) over a 6-month period in a tertiary hepatobiliary surgical center. Two independent researchers analyzed the data to identify HEs and AEs according to two distinct classifications.

Results: A total of 154 HEs (median number per intervention: 7) and 42 AEs (33 minor, 9 major) were identified. All except one major AE were associated with HEs, while 15 minor AEs had no identifiable underlying HEs. The type of HEs significantly varied depending on the presence or absence of AEs. The majority of HEs (n = 128, 83.1%), which did not result in any AEs, primarily involved lapses in attention, whereas approximately half of the AEs were linked to failures in recognition.

Conclusions: This preliminary study indicates that failures in recognition were frequently associated with major AEs during elective liver resection, as per the SDS approach. Larger multicenter studies are necessary to confirm these findings and develop preventive strategies.

Keywords: Data science; Liver; Morbidity; Surgery.

Fig. 1

Relationship between human errors and intraoperative adverse events (AEs).