Objective: To examine the use of surgical intelligence for automatically monitoring critical view of safety (CVS) in laparoscopic cholecystectomy (LC) in a real-world quality initiative. Background: Surgical intelligence encompasses routine, artificial intelligence-based capture and analysis of surgical video, and connection of derived data with patient and outcomes data. These capabilities are applied to continuously assess and improve surgical quality and efficiency in real-world settings. Methods: Laparoscopic cholecystectomies conducted at 2 general surgery departments between December 2022 and August 2023 were routinely captured by a surgical intelligence platform, which identified and continuously presented CVS adoption, surgery duration, complexity, and negative events. In March 2023, the departments launched a quality initiative aiming for 75% CVS adoption. Results: Two hundred seventy-nine procedures were performed during the study. Adoption increased from 39.2% in the 3 preintervention months to 69.2% in the final 3 months (P < 0.001). Monthly adoption rose from 33.3% to 75.7%. Visualization of the cystic duct and artery accounted for most of the improvement; the other 2 components had high adoption throughout. Procedures with full CVS were shorter (P = 0.007) and had fewer events (P = 0.011) than those without. OR time decreased following intervention (P = 0.033). Conclusions: Surgical intelligence facilitated a steady increase in CVS adoption, reaching the goal within 6 months. Low initial adoption stemmed from a single CVS component, and increased adoption was associated with improved OR efficiency. Real-world use of surgical intelligence can uncover new insights, modify surgeon behavior, and support best practices to improve surgical quality and efficiency

Surgical Intelligence Can Lead to Higher Adoption of Best Practices in Minimally Invasive Surgery / Fried, Gm; Ortenzi, M; Dayan, D; Nizri, E; Mirkin, Y; Maril, S; Asselmann, D; Wolf, T. - In: ANNALS OF SURGERY. - ISSN 0003-4932. - 280:3(2024), pp. 525-534. [10.1097/SLA.0000000000006377]

Surgical Intelligence Can Lead to Higher Adoption of Best Practices in Minimally Invasive Surgery

Ortenzi M
Secondo
;
2024-01-01

Abstract

Objective: To examine the use of surgical intelligence for automatically monitoring critical view of safety (CVS) in laparoscopic cholecystectomy (LC) in a real-world quality initiative. Background: Surgical intelligence encompasses routine, artificial intelligence-based capture and analysis of surgical video, and connection of derived data with patient and outcomes data. These capabilities are applied to continuously assess and improve surgical quality and efficiency in real-world settings. Methods: Laparoscopic cholecystectomies conducted at 2 general surgery departments between December 2022 and August 2023 were routinely captured by a surgical intelligence platform, which identified and continuously presented CVS adoption, surgery duration, complexity, and negative events. In March 2023, the departments launched a quality initiative aiming for 75% CVS adoption. Results: Two hundred seventy-nine procedures were performed during the study. Adoption increased from 39.2% in the 3 preintervention months to 69.2% in the final 3 months (P < 0.001). Monthly adoption rose from 33.3% to 75.7%. Visualization of the cystic duct and artery accounted for most of the improvement; the other 2 components had high adoption throughout. Procedures with full CVS were shorter (P = 0.007) and had fewer events (P = 0.011) than those without. OR time decreased following intervention (P = 0.033). Conclusions: Surgical intelligence facilitated a steady increase in CVS adoption, reaching the goal within 6 months. Low initial adoption stemmed from a single CVS component, and increased adoption was associated with improved OR efficiency. Real-world use of surgical intelligence can uncover new insights, modify surgeon behavior, and support best practices to improve surgical quality and efficiency
2024
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/338192
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