Objectives: This study explores how ambulatory medical practices adapted their policies in response to the global COVID-19 crisis. Practice and provider characteristics were used to build an artificial intelligence model that predicts future medical practice closures during critical events.
Methods: We surveyed 261 outpatient medical practices and collected information on clinician age, gender, the protective measures implemented, closure status, and utilization of telemedicine services. These data were used to build an artificial intelligence model that predicts practice closure in future critical public health events.
Results: Responses varied by specialty, location, and physician characteristics. These factors predicted closure status in 85.45% of test cases.
Conclusion: This paper demonstrates that practice characteristics can assist in predicting medical practice responses to public health events.
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| http://dx.doi.org/10.1097/JOM.0000000000003284 | DOI Listing |
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- The study aimed to explore how non-drug treatments affect sarcopenic obesity (SO) through a meta-analysis of randomized controlled trials (RCTs).
- Results indicated that exercise and nutrition can improve various physical health metrics, such as body fat percentage (PBF) and grip strength, but individual interventions showed limited effectiveness.
- The combination of exercise and nutritional interventions proved to be the most effective approach for improving outcomes related to SO, highlighting the potential role of physical therapy as well.
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