Overcoming barriers to the adoption and implementation of predictive modeling and machine learning in clinical care: what can we learn from US academic medical centers?

Abstract

There is little known about how academic medical centers (AMCs) in the US develop, implement, and maintain predictive modeling and machine learning (PM and ML) models. We conducted semi-structured interviews with leaders from AMCs to assess their use of PM and ML in clinical care, understand associated challenges, and determine recommended best practices. Each transcribed interview was iteratively coded and reconciled by a minimum of 2 investigators to identify key barriers to and facilitators of PM and ML adoption and implementation in clinical care. Interviews were conducted with 33 individuals from 19 AMCs nationally. AMCs varied greatly in the use of PM and ML within clinical care, from some just beginning to explore their utility to others with multiple models integrated into clinical care. Informants identified 5 key barriers to the adoption and implementation of PM and ML in clinical care: (1) culture and personnel, (2) clinical utility of the PM and ML tool, (3) financing, (4) technology, and (5) data. Recommendation to the informatics community to overcome these barriers included: (1) development of robust evaluation methodologies, (2) partnership with vendors, and (3) development and dissemination of best practices. For institutions developing clinical PM and ML applications, they are advised to: (1) develop appropriate governance, (2) strengthen data access, integrity, and provenance, and (3) adhere to the 5 rights of clinical decision support. This article highlights key challenges of implementing PM and ML in clinical care at AMCs and suggests best practices for development, implementation, and maintenance at these institutions.

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Citation

Published Version (Please cite this version)

10.1093/jamiaopen/ooz046

Publication Info

Watson, Joshua, Carolyn A Hutyra, Shayna M Clancy, Anisha Chandiramani, Armando Bedoya, Kumar Ilangovan, Nancy Nderitu, Eric G Poon, et al. (2020). Overcoming barriers to the adoption and implementation of predictive modeling and machine learning in clinical care: what can we learn from US academic medical centers?. JAMIA open, 3(2). pp. 167–172. 10.1093/jamiaopen/ooz046 Retrieved from https://hdl.handle.net/10161/21420.

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Scholars@Duke

Bedoya

Armando Diego Bedoya

Assistant Professor of Medicine
Poon

Eric Gon-Chee Poon

Professor of Medicine

I currently serve as the Chief Health Information Officer for Duke Medicine. I also practice primary care internal medicine at the Durham Medical Center as part of Duke Primary Care. In my capacity as CHIO, I am responsible for the visioning and strategic planning of clinical and analytic information systems that impact patient care, research and education.  I work with the Duke Medicine leadership to ensure technology solutions are well aligned with our overall organizational objectives. I oversee the optimization of the Maestro Care (Epic) electronic health record, and partner with physicians, patients and operational leaders to effectively leverage innovative IT in support of the Duke mission.  I also have a keen interest in IT innovation, and work with investigators across Duke to pursue new and innovative ways to efficiently deliver high quality care to our patients.

My research interests have revolved around the use of health information technology to improve the quality of care and patient safety in both the ambulatory and hospital settings.  My work in the ambulatory setting has focused on the efficient delivery of decision support to clinicians to prevent errors of omission and commission during diagnostic test ordering and review of test results. I have also worked to use information technology, including secure on-line patient portals, to improve the communication between clinicians and patients around health maintenance and the follow-up of abnormal test results.  In the inpatient setting, I have conducted several studies to delineate the barriers to and facilitators of the wide-spread diffusion of computerized physician order entry and have led many studies evaluating the safety, financial and socio-technical impact of barcode technology in the hospital pharmacy and nursing units. 


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