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

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.

Racial and Gender Disparities in Utilization of Outpatient Total Shoulder Arthroplasties.

BACKGROUND: Utilization in outpatient total shoulder arthroplasties (TSAs) has increased significantly in recent years. It remains largely unknown whether utilization of outpatient TSA differs across gender and racial groups. This study aimed to quantify racial and gender disparities both nationally and by geographic regions. METHODS: 168,504 TSAs were identified using Medicare fee-for-service (FFS) inpatient and outpatient claims data and beneficiary enrollment data from 2020 to 2022Q4. The percentage of outpatient cases, defined as cases discharged on the same day of surgery, was evaluated by racial and gender groups and by different census divisions. A multivariate logistics regression model controlling for patient socio-demographic information (white vs. non-white race, age, gender, and dual eligibility for both Medicare and Medicaid), hierarchical condition category (HCC) score, hospital characteristics, year fixed effects, and patient residency state fixed effects was performed. RESULTS: The TSA volume per 1000 beneficiaries was 2.3 for the White population compared to 0.8, 0.6 and 0.3 for the Black, Hispanic, and Asian population, respectively. A higher percentage of outpatient TSAs were in White patients (25.6%) compared to Black patients (20.4%) (p < 0.001). The Black TSA patients were also younger, more likely to be female, more likely to be dually eligible for Medicaid, and had higher HCC risk scores. After controlling for patient socio-demographic characteristics and hospital characteristics, the odds of receiving outpatient TSAs were 30% less for Black than the White group (OR 0.70). Variations were observed across different census divisions with South Atlantic (0.67, p < 0.01), East North Central (0.56, p < 0.001), and Middle Atlantic (0.36, p < 0.01) being the four regions observed with significant racial disparities. Statistically significant gender disparities were also found nationally and across regions, with an overall odds ratio of 0.75 (p < 0.001). DISCUSSION: Statistically significant racial and gender disparities were found nationally in outpatient TSAs, with Black patients having 30% (p < 0.001) fewer odds of receiving outpatient TSAs than white patients, and female patients with 25% (p < 0.001) fewer odds than male patients. Racial and gender disparities continue to be an issue for shoulder arthroplasties after the adoption of outpatient TSAs.