Predicting Length of Stay using machine learning for total joint replacements performed at a rural community hospital

• Published in: PloS one Vol. 17; no. 11; p. e0277479
• Main Authors: Sridhar, Srinivasan, Whitaker, Bradley, Mouat-Hunter, Amy, McCrory, Bernadette
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.11.2022; Public Library of Science (PLoS)
• Subjects: Analysis; Arthroplasty, Replacement; Artificial intelligence; Big Data; Biology and Life Sciences; Calculators; Complications and side effects; Costs; Diabetes; Diabetes mellitus; Family income; Feature selection; Health risks; Hospitals; Hospitals, Community; Hospitals, Rural; Humans; Independent variables; Joint replacement; Joint surgery; Knee; Learning algorithms; Length of Stay; Machine Learning; Medical records; Medicine and Health Sciences; Modelling; Orthopedics; Outpatient care facilities; Patient outcomes; Patient satisfaction; Patients; Performance prediction; Pilot Projects; Postoperative Complications; Quality control; Research and Analysis Methods; Rural areas; Rural communities; Social Sciences; Surgical outcomes; United States; United States > US

Predicting patient's Length of Stay (LOS) before total joint replacement (TJR) surgery is vital for hospitals to optimally manage costs and resources. Many hospitals including in rural areas use publicly available models such as National Surgical Quality Improvement Program (NSQIP) calculator w...