{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T23:57:44Z","timestamp":1793404664577,"version":"4.5.0"},"reference-count":26,"publisher":"Scientific Scholar","content-domain":{"domain":["surgicalneurologyint.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:sec id=\"st1\">\n<jats:title>Background: <\/jats:title>\n<jats:p>Estimating operative time with low accuracy and precision leads to suboptimal scheduling of available operating rooms (ORs) and waste of healthcare resources. We aimed to develop a predictive machine learning (ML) model for operative time using our institutional data to improve the scheduling of neurosurgical ORs, minimize waste, and maximize resource utilization.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"st2\">\n<jats:title>Methods: <\/jats:title>\n<jats:p>We developed a predictive model using our institution\u2019s multicenter registry, which included diverse neurosurgical cases across three tertiary hospitals. We applied multiple ML techniques, including linear regression, support vector regression, deep neural networks, and extreme gradient boosting regression (XGBR). The best model was combined with the first fit decreasing bin-packing algorithm to create a practical OR scheduling system based on prior data.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"st3\">\n<jats:title>Results: <\/jats:title>\n<jats:p>The XGBR model exhibited the best performance, with lower mean absolute errors (MAEs) (root mean squared error: 52.24, MAE: 37.25) and higher R2 (0.76) values than other models. Implementing this model in a simulated OR scheduling scenario using institutional data, we successfully scheduled 30 random procedures with minimal residual time, such that the model ran out of procedures for the week with 86 and 171 min remaining in the cranial and spinal ORs, respectively, demonstrating potential efficiency gains. The cranial procedures were scheduled over 4 days and spinal procedures over 6 days, optimizing available OR time.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"st4\">\n<jats:title>Conclusion: <\/jats:title>\n<jats:p>Integrating ML models into operative time prediction can significantly improve the accuracy and efficiency of surgical schedules. This approach minimizes wasted OR time and enhances resource allocation, potentially improving patient outcomes and satisfaction by reducing waiting times.<\/jats:p>\n<\/jats:sec>","DOI":"10.25259\/sni_626_2025","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T06:02:45Z","timestamp":1758261765000},"page":"395","update-policy":"https:\/\/doi.org\/10.25259\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Optimizing neurosurgical operating room schedule: Integrating machine learning models for operative time prediction and schedule design"],"prefix":"10.25259","volume":"16","author":[{"given":"Karim Rizwan","family":"Nathani","sequence":"first","affiliation":[{"name":"Department of Neurologic Surgery, Mayo Clinic, Rochester, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura A.","family":"Machlab","sequence":"additional","affiliation":[{"name":"Department of Neurologic Surgery, Mayo Clinic, 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