{"id":"AIBD-0611","title":"Machine learning case-duration predictions improved surgical scheduling accuracy and cut patient wait time at Memorial Sloan Kettering","summary":"In a 2018 Memorial Sloan Kettering randomized trial of 683 surgeries, a machine learning model cut scheduling error by 9.8 minutes and patient waits by 33.1 minutes.","date_observed":"2018-06-25","date_basis":"event","date_precision":"day","benefit_status":"reported","source_collection":null,"source_entry":null,"account":"Memorial Sloan Kettering Cancer Center in New York randomized colorectal and gynecology operations scheduled between 7 April and 25 June 2018 on two surgical campuses. For half of the cases, schedulers used case durations predicted by an in-house machine learning model the day before surgery; the other half used the usual EHR estimate adjusted by surgeons or schedulers. Among 683 patients, the mean absolute error of the planned duration fell from 59.3 minutes to 49.5 minutes. The start delay for following cases, which is the time patients waited in the presurgical area, fell by 33.1 minutes per patient, from 49.4 to 16.3 minutes. Time between cases did not change. The model was built and tested by the hospital's own staff at a single cancer center, and the trial covered two surgical services.","category":"general benefit","unexpected":false,"purpose":"The model was built to predict how long each operation will take so that schedulers can plan rooms and patient arrival times. Shorter patient waits follow from that purpose.","system":"Memorial Sloan Kettering machine learning surgical case-duration prediction model","model_version":null,"developer":"Memorial Sloan Kettering Cancer Center (Department of Strategy and Innovation)","deployer":"Memorial Sloan Kettering Cancer Center","location":"Memorial Sloan Kettering main campus and Josie Robertson Surgical Center, New York, United States","domain":"Healthcare","segment":null,"benefit_type":"Improved Task Accuracy","taxonomy_note":null,"registry_ids":{},"ratings":[{"group":"personal","depth":1,"reason":"Patients waited 33.1 fewer minutes on average in the presurgical area."},{"group":"organizational","depth":2,"reason":"Mean absolute error of planned case duration fell from 59.3 to 49.5 minutes."}],"ai_role":{"status":"assessed","statement":"Cases were randomized to model-based or usual duration estimates, stratified by surgeon and operating room location, so the comparison isolates the model's estimates."},"evidence":[{"kind":"peer-reviewed study","citation":"Spence C, Shah OA, et al. Machine learning models to predict surgical case duration compared to current industry standards: scoping review. BJS Open 2023;7(6):zrad113. doi:10.1093/bjsopen/zrad113. Independent review by authors at the South West London Elective Orthopaedic Centre.","title":"Machine learning models to predict surgical case duration compared to current industry standards: scoping review","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10630142/","date":"2023-11-03","written_by":"independent","quote":"Implementation of an ML model significantly improved accuracy in predicting case duration and led to reduced patient wait-time"},{"kind":"peer-reviewed study","citation":"Strömblad CT, Baxter-King RG, Meisami A, et al. Effect of a Predictive Model on Planned Surgical Duration Accuracy, Patient Wait Time, and Use of Presurgical Resources: A Randomized Clinical Trial. JAMA Surgery 2021;156(4):315-321. doi:10.1001/jamasurg.2020.6361. Written by the hospital team that built the model.","title":"Effect of a Predictive Model on Planned Surgical Duration Accuracy, Patient Wait Time, and Use of Presurgical Resources: A Randomized Clinical Trial","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC7841577/","date":"2021-01-27","written_by":"makers","quote":"The overall mean (SD) reduction of wait time was 33.1 minutes per patient (from 49.4 minutes to 16.3 minutes per patient)."}],"replication":{"produced_by":["The model drew on more than 300 data points per case from the EHR.","Predictions ran each morning one weekday before surgery and went to schedulers."],"must_hold":["Data for each case are available before scheduling.","Schedulers apply the predicted durations when they set patient times."],"echoes":[]},"paired_incident":null,"headline":"Surgery-length predictions shortened patient waits at Sloan Kettering","metric":"fell by 33.1 minutes per patient, from 49.4 to 16.3 minutes","comparator":{"against":"Cases randomized to the usual EHR estimate with surgeon or scheduler adjustment","before":"Mean absolute error of 59.3 minutes","after":"Mean absolute error of 49.5 minutes","basis":"measured","quote":"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.","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC7841577/"},"later":{"status":"unknown","date":"2021-01-27","note":"The authors wrote that they planned to extend the model to all surgical cases; the cited sources do not confirm that rollout.","quote":"These findings have led us to pursue systemwide implementation of this model in which all of our institution's 27 000 annual surgical cases will leverage the proposed predictive modeling framework.","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC7841577/"},"value":null,"developer_country":"United States","deployer_country":"United States","unrated_because":null,"brief_notes":null,"brief":null,"revisions":[{"date":"2026-10-29","change":"Added the case.","source":"https://pmc.ncbi.nlm.nih.gov/articles/PMC10630142/","editor":"AIBD"}],"confidence":"studied","shade":"high","claim_status":"Independently studied","claim_basis":"Sources: one independent peer-reviewed study and the makers' peer-reviewed study. 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