Accepted for/Published in: JMIR Human Factors
Date Submitted: Apr 18, 2025
Open Peer Review Period: Apr 21, 2025 - Jun 16, 2025
Date Accepted: Jun 26, 2025
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
User-centered delivery of AI-powered healthcare technologies in clinical settings: mixed-methods case study
ABSTRACT
Background:
Providers spend a large percentage of their day using EHR technology, and frequently report frustration when EHR tasks are time-consuming and effortful. To solve these challenges, artificial intelligence-based enhancements to EHR technology are increasingly being deployed. However, AI-based implementations for EHR features often lack user-centered evaluations.
Objective:
Through a user-centered approach, this research program evaluates the implementation of an AI-powered search and clinical discovery tool within an EHR.
Methods:
We conducted a mixed-methods study consisting of interviews, observations, and surveys over a period of five months.
Results:
High adoption rates for the AI-based features (93% of users after 3 months) and positive feedback across key metrics (user satisfaction, product helpfulness, perception of time saved) demonstrated that our tool was not only successfully integrated into various clinical workflows, but also improved the user experience for clinicians.
Conclusions:
Our results underscore the feasibility and effectiveness of utilizing a user-centered approach for deployment of clinical AI tools. High adoption rates and positive user experiences were driven by our user-centered research program, which emphasized close collaboration with users, rapid incorporation of feedback and tailored user training. This work can be utilized as a template for the design and integration of human-centered research for AI-tool deployment in clinical settings.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.