TY - JOUR
T1 - Utility of Machine Learning, Natural Language Processing, and Artificial Intelligence in Predicting Hospital Readmissions after Orthopaedic Surgery
T2 - A Systematic Review and Meta-Analysis
AU - Fares, Mohamad Y.
AU - Liu, Harry H.
AU - Da Silva Etges, Ana Paula Beck
AU - Zhang, Benjamin
AU - Warner, Jon J.P.
AU - Olson, Jeffrey J.
AU - Fedorka, Catherine J.
AU - Khan, Adam Z.
AU - Best, Matthew J.
AU - Kirsch, Jacob M.
AU - Simon, Jason E.
AU - Sanders, Brett
AU - Costouros, John G.
AU - Zhang, Xiaoran
AU - Jones, Porter
AU - Haas, Derek A.
AU - Abboud, Joseph A.
AU - Armstrong, April D.
AU - Belniak, Robert M.
AU - Gottschalk, Michael B.
AU - Makhni, Eric C.
AU - Mazzocca, Augustus
AU - Macdonald, Peter
AU - O'Donnell, Evan A.
AU - Srikumaran, Uma
AU - Stieler, Evan
AU - Updegrove, Gary F.
AU - Wagner, Eric R.
AU - Woodmass, Jarret
N1 - Publisher Copyright:
Copyright © 2024 by The Journal of Bone and Joint Surgery, Incorporated.
PY - 2024/8/22
Y1 - 2024/8/22
N2 - 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.
AB - 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.
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U2 - 10.2106/JBJS.RVW.24.00075
DO - 10.2106/JBJS.RVW.24.00075
M3 - Article
C2 - 39172864
AN - SCOPUS:85201689792
SN - 2329-9185
VL - 12
JO - JBJS Reviews
JF - JBJS Reviews
IS - 8
M1 - 00011
ER -