Publication Date
4-3-2024
Journal
Journal of the American Medical Informatics Association
DOI
10.1093/jamia/ocae017
PMID
38341800
PMCID
PMC10990545
PubMedCentral® Posted Date
2-11-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Keywords
Humans, Ambulatory Care Facilities, Artificial Intelligence, Communication, Heart Failure, Information Technology, artificial intelligence, heart failure, home monitoring, computerized decision support, implementation
Abstract
OBJECTIVES: We conducted an implementation planning process during the pilot phase of a pragmatic trial, which tests an intervention guided by artificial intelligence (AI) analytics sourced from noninvasive monitoring data in heart failure patients (LINK-HF2).
MATERIALS AND METHODS: A mixed-method analysis was conducted at 2 pilot sites. Interviews were conducted with 12 of 27 enrolled patients and with 13 participating clinicians. iPARIHS constructs were used for interview construction to identify workflow, communication patterns, and clinician's beliefs. Interviews were transcribed and analyzed using inductive coding protocols to identify key themes. Behavioral response data from the AI-generated notifications were collected.
RESULTS: Clinicians responded to notifications within 24 hours in 95% of instances, with 26.7% resulting in clinical action. Four implementation themes emerged: (1) High anticipatory expectations for reliable patient communications, reduced patient burden, and less proactive provider monitoring. (2) The AI notifications required a differential and tailored balance of trust and action advice related to role. (3) Clinic experience with other home-based programs influenced utilization. (4) Responding to notifications involved significant effort, including electronic health record (EHR) review, patient contact, and consultation with other clinicians.
DISCUSSION: Clinician's use of AI data is a function of beliefs regarding the trustworthiness and usefulness of the data, the degree of autonomy in professional roles, and the cognitive effort involved.
CONCLUSION: The implementation planning analysis guided development of strategies that addressed communication technology, patient education, and EHR integration to reduce clinician and patient burden in the subsequent main randomized phase of the trial. Our results provide important insights into the unique implications of implementing AI analytics into clinical workflow.
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Biochemistry, Biophysics, and Structural Biology Commons, Cardiology Commons, Cardiovascular Diseases Commons, Medical Sciences Commons
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