Factors governing the adoption of artificial intelligence in healthcare providers

Abstract

Artificial intelligence applications are prevalent in the research lab and in startups, but relatively few have found their way into healthcare provider organizations. Adoption of AI innovations in consumer and business domains is typically much faster. While such delays are frustrating to those who believe in the potential of AI to transform healthcare, they are largely inherent in the structure and function of provider organizations. This article reviews the factors that govern adoption and explains why adoption has taken place at a slow pace. Research sources for the article include interviews with provider executives, healthcare IT professors and consultants, and AI vendor executives. The article considers differential speed of adoption in clinical vs. administrative applications, regulatory approval issues, reimbursement and return on investments in healthcare AI, data sources and integration with electronic health record systems, the need for clinical education, issues involving fit with clinical workflows, and ethical considerations. It concludes with a discussion of how provider organizations can successfully plan for organizational deployment.

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