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Cautious Optimism Building: What HIE Managers Think About Adding Artificial Intelligence to Improve Patient Matching
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DOI：10.3390/socsci14100579.png)
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Abstract
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Each year an estimated 440,000 medical errors occur in the U.S., of which 38% are a direct result of patient matching errors. As patients seek care in medical facilities, their records are often dispersed. Health Information Exchanges (HIEs) strive to retrieve and consolidate these records and as such, accurate matching of patient data becomes a critical prerequisite. Artificial intelligence (AI) is increasingly being seen as a potential solution to this vexing challenge. We present findings from an exploratory field study involving interviews with 27 HIE executives across the U.S. on tensions they are sensing and balancing in incorporating AI in patient matching processes. Our analysis of data from the interviews reveals, on the one hand, significant optimism regarding AI's capacity to improve matching processes, and on the other, concerns due to the risks associated with algorithmic biases, uncertainties regarding AI-based decision-making, and implementation hurdles such as costs, the need for specialized talent, and insufficient datasets for training AI models. We conceptualize this dialectical tension in the form of a grounded theory framework on Cautious AI Optimism.
Keywords:
health information exchange
patient matching
artificial intelligence
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