Effect of a Predictive Model on Planned Surgical Duration Accuracy, Patient Wait Time, and Use of Presurgical ResourcesA Randomized Clinical Trial

Importance Accurate surgical scheduling affects patients, clinical staff, and use of physical resources. Although numerous retrospective analyses have suggested a potential for improvement, the real-world outcome of implementing a machine learning model to predict surgical case duration appears not...

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Veröffentlicht in:Archives of surgery (Chicago. 1960) 2021-04, Vol.156 (4), p.315
Hauptverfasser: Strömblad, Christopher T, Baxter-King, Ryan G, Meisami, Amirhossein, Yee, Shok-Jean, Levine, Marcia R, Ostrovsky, Aaron, Stein, Daniel, Iasonos, Alexia, Weiser, Martin R, Garcia-Aguilar, Julio, Abu-Rustum, Nadeem R, Wilson, Roger S
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Zusammenfassung:Importance Accurate surgical scheduling affects patients, clinical staff, and use of physical resources. Although numerous retrospective analyses have suggested a potential for improvement, the real-world outcome of implementing a machine learning model to predict surgical case duration appears not to have been studied. Objectives To assess accuracy and real-world outcome from implementation of a machine learning model that predicts surgical case duration. Design, Setting, and Participants This randomized clinical trial was conducted on 2 surgical campuses of a cancer specialty center. Patients undergoing colorectal and gynecology surgery at Memorial Sloan Kettering Cancer Center who were scheduled more than 1 day before surgery between April 7, 2018, and June 25, 2018, were included. The randomization process included 29 strata (11 gynecological surgeons at 2 campuses and 7 colorectal surgeons at a single campus) to ensure equal chance of selection for each surgeon and each campus. Patients undergoing more than 1 surgery during the study's timeframe were enrolled only once. Data analyses took place from July 2018 to November 2018. Interventions Cases were assigned to machine learning–assisted surgical predictions 1 day before surgery and compared with a control group. Main Outcomes and Measures The primary outcome measure was accurate prediction of the duration of each scheduled surgery, measured by (arithmetic) mean (SD) error and mean absolute error. Effects on patients and systems were measured by start time delay of following cases, the time between cases, and the time patients spent in presurgical area. Results A total of 683 patients were included (mean [SD] age, 55.8 [13.8] years; 566 women [82.9%]); 72 were excluded. Of the 683 patients included, those assigned to the machine learning algorithm had significantly lower mean (SD) absolute error (control group, 59.3 [72] minutes; intervention group, 49.5 [66] minutes; difference, −9.8 minutes; P = .03) compared with the control group. Mean start-time delay for following cases (patient wait time in a presurgical area), dropped significantly: 62.4 minutes (from 70.2 minutes to 7.8 minutes) and 16.7 minutes (from 36.9 minutes to 20.2 minutes) for patients receiving colorectal and gynecology surgery, respectively. The overall mean (SD) reduction of wait time was 33.1 minutes per patient (from 49.4 minutes to 16.3 minutes per patient). Improved accuracy did not adversely inflate time between cases (surgeo
ISSN:2168-6254
2168-6262
DOI:10.1001/jamasurg.2020.6361