Accepted for/Published in: JMIR Formative Research
Date Submitted: May 12, 2025
Open Peer Review Period: May 12, 2025 - Jul 7, 2025
Date Accepted: Oct 14, 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Warning: This is an author submission that is not peer-reviewed or edited.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.
Bridging ED Fall Risk Screening to Community Prevention: An Automated Digital Intervention Using a Chatbot
ABSTRACT
Background:
Emergency departments (EDs) routinely screen for fall risk, but patients are rarely notified of their results or referred to prevention resources often due to competing clinical demands. Chatbots can be used to provide patient education and community resources in a conversational, friendly manner that does not add to clinician workload. We developed and implemented an automated intervention using our health system’s artificial intelligence (AI) chatbot, Livi, to address this gap in fall prevention across 17 EDs.
Objective:
The objective of this study is to outline the process of developing a tool that automatically connects older ED patients who screen high risk for falls to fall prevention resources near their homes using an AI chatbot.
Methods:
We worked with electronic health record (EHR) and ED operations teams to embed a process that delivers a quick response (QR) code in the After Visit Summary of high-risk patients. Scanning the QR code launches a conversation with Livi, guiding users to evidence-based, free or low-cost fall prevention resources where they live. We conducted rapid, iterative usability testing of the Livi falls chatbot with community members (n=93) during the development process.
Results:
Rapid iterative testing led to enhancements such as increased font size, option for Spanish language, additional geographic locations for fall prevention resources, home modification resources, the ability to self-assess for fall risk, fall prevention tips, and the ability for patients to leave feedback on the Livi chatbot. Because all EDs in the health system use the same instance of Epic, the EHR workflow wasd eployed system-wide instantaneously. The use of a QR code linked with Livi also allows for rapid updating of prevention resources.
Conclusions:
This scalable, EHR-integrated intervention demonstrates a novel approach to improving population health by capitalizing on existing clinical workflows and automating both risk notification and personalized resource referral for older adults without increasing clinician burden.
Citation
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Copyright
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