Turning artificial intelligence into impact: An action plan for providers

John W. Showalter and Trey La Charité
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John W. Showalter: Adjunct Professor, George Washington University, USA

Management in Healthcare: A Peer-Reviewed Journal, 2018, vol. 2, issue 3, 198-206

Abstract: The shortcomings of predictive analytic solutions within healthcare are coming into focus. As a result, more providers are looking to artificial intelligence (AI)-based machines that more effectively and precisely identify patients at risk of an event and the actions that will reduce that risk. The challenge with these machines is adoption. And while user engagement has always been a barrier in technology implementation, the compelling event driving adoption is changing with the entry of AI machines focused on improving patient outcomes. Unlike the mandated projects of the past, these machines rely on an organisation and individual’s drive to stop adverse events and improve quality outcomes. This paper examines the changing analytic landscape within healthcare through the University of Tennessee Medical Center’s own journey with the Cognitive Clinical Success Machine. It provides signposts for any provider looking to incorporate the power of cognitive machines into their organisation. The authors outline the important considerations that should guide a provider while evaluating AI machine solutions, including the essential value levers that drive return on investment.

Keywords: predictive analytics; artificial intelligence; cognitive machine learning; emerging technology adoption (search for similar items in EconPapers)
JEL-codes: I1 I10 (search for similar items in EconPapers)
Date: 2018
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