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Date

2022-01-04

Authors

Weber, Michael

Limmer, Nicolas

Weking, Jörg

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Artificial Intelligence (AI) arguably represents a key technology for the digitalization of health care. Specifically, health insurers can benefit from AI as they typically have access to vast amounts of data. However, practitioners struggle to adopt AI in productive use, and extant research lacks an overview of use cases for AI in health insurance as well as prioritization criteria that can guide their implementation. To address this gap, we conduct explorative interviews in the context of the German statutory health insurance system. We identify AI use cases in the areas of predictive health, individualized service, anomaly detection, and operations enhancement. We find that health insurers are likely to prioritize these use cases according to implementation complexity and business orientation, whereas focusing on simple use cases that target cost savings is recommended by experts. Our study advances the understanding of AI adoption in health insurance and supports practitioners in guiding future AI initiatives.

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IT Adoption, Diffusion, and Evaluation in Healthcare, adoption, artificial intelligence, healthcare, health insurance, prioritization

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http://hdl.handle.net/10125/79818

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10 pages

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Proceedings of the 55th Hawaii International Conference on System Sciences

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Attribution-NonCommercial-NoDerivatives 4.0 International

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IT Adoption, Diffusion, and Evaluation in Healthcare