Скачать с ютуб Use and Evaluation of Predictive AI in US Hospitals в хорошем качестве

Use and Evaluation of Predictive AI in US Hospitals

Paige Nong - Assistant Professor, Division of Health Policy and Management, University of Minnesota Effective evaluation and governance of predictive models used in health care, particularly those driven by artificial intelligence (AI) and machine learning, are needed to ensure that models are fair, appropriate, valid, effective, and safe. We analyzed data from the 2023 American Hospital Association Annual Survey Information Technology Supplement to identify how AI and predictive models are used and evaluated for accuracy and bias in hospitals. Hospitals use AI and predictive models to predict health trajectories or risks for inpatients, identify high-risk outpatients to inform follow-up care, monitor health, recommend treatments, simplify or automate billing procedures, and facilitate scheduling. We found that 65 percent of US hospitals used predictive models, and 79 percent of those used models from their electronic health record developer. Sixty-one percent of hospitals that used models evaluated them for accuracy using data from their health system (local evaluation), but only 44 percent reported local evaluation for bias. Hospitals that developed their own predictive models, had high operating margins, and were health system members were more likely to report local evaluation. Policy and programs that provide technical support, tools to assess FAVES principles, and educational resources would help ensure that all hospitals can use predictive models safely and prevent a new organizational digital divide in AI. Captions are auto-generated and may contain errors.