ALERT-ED: Awareness and Linkage to Resources for At-Risk Emergency Department Patients: Interviews with Older Patients, Caregivers, and Nurses.

Abstract

Falls are a leading cause of morbidity among older adults. Although emergency departments (EDs) routinely screen for fall risk, few patients are connected to prevention programs after discharge. Digital tools could automate notification and referral, yet few have been developed or tested in acute care settings with ED end-users in mind. To assess the feasibility, acceptability, and usability of ALERT-ED, a novel digital intervention designed to automate fall risk notification and referral. We conducted semi-structured interviews with English- and Spanish-speaking ED patients aged ≥ 65, caregivers, and nurses across multiple hospitals within a large integrated healthcare system. ALERT-ED automatically embeds a QR code in the After Visit Summary of high fall risk ED patients, linking to a chatbot (Livi) that supports self-screening, fall education, and referral to community programs. Thematic analysis of interview transcripts informed optimization of the intervention ahead of a planned clinical trial. Interviews of 6 nurses, 12 patients, and 4 caregivers revealed seven themes: (1) feasibility depends on dissemination strategies and workflow alignment; (2) barriers include limited awareness and nursing time constraints; (3) acceptability improves trust, readability, and personalization; (4) expanding scope to enhance engagement; (5) privacy and artificial intelligence skepticism persist; (6) usability is high due to the QR interface; and (7) accessibility gaps remain for users with impairments. ALERT-ED demonstrates strong feasibility, acceptability, and usability among older adults, caregivers, and ED nurses. By automating fall risk notification and referral, ALERT-ED offers a pathway for integrating public health interventions into emergency care.