How Health Systems Decide to Use Artificial Intelligence for Clinical Decision Support

Despite the widespread enthusiasm for artificial intelligence (AI) in health care, adoption of AI-enabled clinical-decision support software (CDS) that supports the detection, diagnosis, and treatment of health conditions in specific patients, as well as broader population health management tools, remains slow. Although previous research has examined the challenges associated with adopting AI and the factors that drive adoption, this article explores the perspectives of multiple health systems that have adopted AI-enabled CDS, with a particular focus on how they evaluate the business case for AI-enabled CDS products. This article outlines how health systems choose which software products to deploy, the major cost factors that health systems consider when operationalizing AI-enabled CDS products, and how the value proposition of the software products varies across health systems. The authors found that adoption of AI is driven by a variety of considerations, including clinical utility, ease of use, and patient safety, but also hospital priorities, interoperability/ease of software integration, physician champions, payment models, and market dynamics.