Utility of Machine Learning, Natural Language Processing, and Artificial Intelligence in Predicting Hospital Readmissions After Orthopaedic Surgery: A Systematic Review and Meta-Analysis.

Autor:	Fares MY; Rothman Institute, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania., Liu HH; Avant-garde Health, Boston, Massachusetts., da Silva Etges APB; Avant-garde Health, Boston, Massachusetts., Zhang B; Brigham and Women's Hospital, Boston, Massachusetts., Warner JJP; Department of Orthopaedic Surgery, Harvard Medical School, Boston Shoulder Institute, Massachusetts General Hospital, Boston, Massachusetts., Olson JJ; Hartford Healthcare, Hartford, Connecticut., Fedorka CJ; Cooper Bone and Joint Institute, Cooper University Hospital, Camden, New Jersey., Khan AZ; Department of Orthopaedic Surgery, Southern California Permanente Medical Group, Panorama City, California., Best MJ; Department of Orthopaedic Surgery, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, Maryland., Kirsch JM; Department of Orthopaedic Surgery, New England Baptist Hospital, Tufts University School of Medicine, Boston, Massachusetts., Simon JE; Department of Orthopaedic Surgery, Massachusetts General Hospital/Newton-Wellesley Hospital, Boston, Massachusetts., Sanders B; Center for Sports Medicine and Orthopaedics, Chattanooga, Tennessee., Costouros JG; Institute for Joint Restoration and Research, California Shoulder Center, Menlo Park, California., Zhang X; Avant-garde Health, Boston, Massachusetts., Jones P; Avant-garde Health, Boston, Massachusetts., Haas DA; Avant-garde Health, Boston, Massachusetts., Abboud JA; Rothman Institute, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania.
Jazyk:	angličtina
Zdroj:	JBJS reviews [JBJS Rev] 2024 Aug 22; Vol. 12 (8). Date of Electronic Publication: 2024 Aug 22 (Print Publication: 2024).
DOI:	e24.00075
Abstrakt:	Background: Numerous applications and strategies have been utilized to help assess the trends and patterns of readmissions after orthopaedic surgery in an attempt to extrapolate possible risk factors and causative agents. The aim of this work is to systematically summarize the available literature on the extent to which natural language processing, machine learning, and artificial intelligence (AI) can help improve the predictability of hospital readmissions after orthopaedic and spine surgeries. Methods: This is a systematic review and meta-analysis. PubMed, Embase and Google Scholar were searched, up until August 30, 2023, for studies that explore the use of AI, natural language processing, and machine learning tools for the prediction of readmission rates after orthopedic procedures. Data regarding surgery type, patient population, readmission outcomes, advanced models utilized, comparison methods, predictor sets, the inclusion of perioperative predictors, validation method, size of training and testing sample, accuracy, and receiver operating characteristics (C-statistic), among other factors, were extracted and assessed. Results: A total of 26 studies were included in our final dataset. The overall summary C-statistic showed a mean of 0.71 across all models, indicating a reasonable level of predictiveness. A total of 15 articles (57%) were attributed to the spine, making it the most commonly explored orthopaedic field in our study. When comparing accuracy of prediction models between different fields, models predicting readmissions after hip/knee arthroplasty procedures had a higher prediction accuracy (mean C-statistic = 0.79) than spine (mean C-statistic = 0.7) and shoulder (mean C-statistic = 0.67). In addition, models that used single institution data, and those that included intraoperative and/or postoperative outcomes, had a higher mean C-statistic than those utilizing other data sources, and that include only preoperative predictors. According to the Prediction model Risk of Bias Assessment Tool, the majority of the articles in our study had a high risk of bias. Conclusion: AI tools perform reasonably well in predicting readmissions after orthopaedic procedures. Future work should focus on standardizing study methodologies and designs, and improving the data analysis process, in an attempt to produce more reliable and tangible results. Level of Evidence: Level III. See Instructions for Authors for a complete description of levels of evidence. Competing Interests: Disclosure: The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJSREV/B118). (Copyright © 2024 by The Journal of Bone and Joint Surgery, Incorporated.)
Databáze:	MEDLINE
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