{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T18:58:20Z","timestamp":1785265100883,"version":"3.55.0"},"reference-count":27,"publisher":"Ovid Technologies (Wolters Kluwer Health)","content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"\n Background:<\/jats:title>\n Efficient allocation of operating room (OR) time is critical in trauma centers, where unpredictable volumes lead to wasted resources. Forecasting trauma volume is challenging, but integrating exogenous factors with historical patterns may enable proactive scheduling tailored to daily operative demand. This study develops time-series and machine learning models to predict daily orthopaedic trauma operative caseloads.<\/jats:p>\n <\/jats:sec>\n \n Methods:<\/jats:title>\n Data for patients undergoing operative management for orthopaedic trauma from January 2012 to December 2023 were aggregated. Models predicted daily caseload in hours using features from hospital records plus regional population and environmental data. Data were split in chronological order into the training set (70%), the validation set (15%), and the test set (15%). Time-series and machine learning models were trained to minimize the mean absolute error (MAE). We evaluated the model\u2019s accuracy in predicting 7-hour and 8-hour clinical volume thresholds, using 95% predictive intervals (PIs), and compared its performance with rolling averages and an intuitive model.<\/jats:p>\n <\/jats:sec>\n \n Results:<\/jats:title>\n A total of 9,637 procedures were included. The mean daily caseload was 8.17 \u00b1 5.38 hours. Pattern mining and discovery seasonal autoregressive integrated moving average (PMD-SARIMA) with manually selected features achieved the lowest test MAE of 1.80 hours, outperforming rolling averages over 7 days (MAE, 4.37 hours), 14 days (MAE, 4.23 hours), and 30 days (MAE, 4.15 hours) and intuitive modeling (MAE, 4.73 hours). Most machine learning and time-series models correctly predicted whether daily caseload would exceed an 8-hour OR block \u226585% (95% PI, 77% to 96%) of the time, substantially outperforming rolling average baselines (49% to 57%).<\/jats:p>\n <\/jats:sec>\n \n Conclusions:<\/jats:title>\n Integrating exogenous environmental, temporal, and system-level factors with historical trauma patterns enabled accurate forecasting of daily orthopaedic trauma caseload, substantially outperforming current models. This framework serves as a template that trauma centers can calibrate to their local context to support OR scheduling, staffing, and resource allocation.<\/jats:p>\n <\/jats:sec>\n \n Clinical Relevance:<\/jats:title>\n This predictive framework enables trauma centers to proactively allocate OR time and surgical staff in response to anticipated daily demand. By optimizing these operational resources, hospitals can minimize surgical delays and ensure timely, efficient care delivery for patients with orthopaedic trauma.<\/jats:p>\n <\/jats:sec>","DOI":"10.2106\/jbjs.26.00014","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T18:00:42Z","timestamp":1785261642000},"update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Transforming Orthopaedic Trauma Care: Forecasting Operating Room Demand by Harnessing Time-Series Analysis and Machine Learning"],"prefix":"10.2106","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7414-1701","authenticated-orcid":false,"given":"Aazad","family":"Abbas","sequence":"first","affiliation":[{"name":"Division of Orthopaedic Surgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada"},{"name":"Holland Bone & Joint Program, Sunnybrook Research Institute, Toronto, Ontario, 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