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.

How urban form impacts flooding.

Urbanization and climate change are contributing to severe flooding globally, damaging infrastructure, disrupting economies, and undermining human well-being. Approaches to make cities more resilient to floods are emerging, notably with the design of flood-resilient structures, but relatively little is known about the role of urban form and its complexity in the concentration of flooding. We leverage statistical mechanics to reduce the complexity of urban flooding and develop a mean-flow theory that relates flood hazards to urban form characterized by the ground slope, urban porosity, and the Mermin order parameter which measures symmetry in building arrangements. The mean-flow theory presents a dimensionless flood depth that scales linearly with the urban porosity and the order parameter, with different scaling for disordered square- and hexagon-like forms. A universal scaling is obtained by introducing an effective mean chord length representative of the unobstructed downslope travel distance for flood water, yielding an analytical model for neighborhood-scale flood hazards globally. The proposed mean-flow theory is applied to probe city-to-city variations in flood hazards, and shows promising results linking recorded flood losses to urban form and observed rainfall extremes.

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.

Complex adaptive systems-based framework for modeling the health impacts of climate change.

Introduction: Climate change is a global phenomenon with far-reaching consequences, and its impact on human health is a growing concern. The intricate interplay of various factors makes it challenging to accurately predict and understand the implications of climate change on human well-being. Conventional methodologies have limitations in comprehensively addressing the complexity and nonlinearity inherent in the relationships between climate change and health outcomes. Objectives: The primary objective of this paper is to develop a robust theoretical framework that can effectively analyze and interpret the intricate web of variables influencing the human health impacts of climate change. By doing so, we aim to overcome the limitations of conventional approaches and provide a more nuanced understanding of the complex relationships involved. Furthermore, we seek to explore practical applications of this theoretical framework to enhance our ability to predict, mitigate, and adapt to the diverse health challenges posed by a changing climate. Methods: Addressing the challenges outlined in the objectives, this study introduces the Complex Adaptive Systems (CAS) framework, acknowledging its significance in capturing the nuanced dynamics of health effects linked to climate change. The research utilizes a blend of field observations, expert interviews, key informant interviews, and an extensive literature review to shape the development of the CAS framework. Results and discussion: The proposed CAS framework categorizes findings into six key sub-systems: ecological services, extreme weather, infectious diseases, food security, disaster risk management, and clinical public health. The study employs agent-based modeling, using causal loop diagrams (CLDs) tailored for each CAS sub-system. A set of identified variables is incorporated into predictive modeling to enhance the understanding of health outcomes within the CAS framework. Through a combination of theoretical development and practical application, this paper aspires to contribute valuable insights to the interdisciplinary field of climate change and health. Integrating agent-based modeling and CLDs enhances the predictive capabilities required for effective health outcome analysis in the context of climate change. Conclusion: This paper serves as a valuable resource for policymakers, researchers, and public health professionals by employing a CAS framework to understand and assess the complex network of health impacts associated with climate change. It offers insights into effective strategies for safeguarding human health amidst current and future climate challenges.