- ED-to-community transitions depend on relationship-driven, locally embedded workflows, manual effort, and clinician knowledge rather than robust system supports.
- Information systems are fragmented, outdated, and under-trusted, limiting resource visibility, referral tracking, and post-discharge outcome monitoring.
- Design priorities for digital and AI tools include trusted resource directories, wait-time visibility, referral tracking, shared psychosocial notes, and verified resource filtering.
JMIR Form Res. 2026 Nov 2. doi: 10.2196/104833. Online ahead of print.
ABSTRACT
BACKGROUND: Emergency departments (EDs) are critical access points for people experiencing mental health crises, yet transitions from the ED to community-based care often break down. Mental Health Clinicians (MHCs) play a key role in supporting discharge planning and referral coordination, work that occurs within fragmented information environments. Digital and artificial intelligence (AI)-supported tools may ease this burden, but little is known about which tools MHCs would trust and use in high-stakes care transitions.
OBJECTIVE: This study aimed to explore how MHC key informants described ED-to-community mental health transition workflows and identify preliminary considerations for future design and implementation of digital or AI-supported transition tools.
METHODS: We conducted exploratory key informant interviews using rapid qualitative methods. Four MHCs with direct experience supporting ED or urgent mental health discharge planning across three large academic hospital settings in Toronto, Ontario, participated in individual semi-structured interviews over Zoom. The interview guide was informed by service blueprinting concepts and explored transition workflows, decision points, information needs, tools used, referral pain points, and perceptions of digital or AI-supported tools. Data were analyzed using rapid qualitative analysis. Structured summaries were organized into matrices, including a current-state service blueprint and technology-supported design opportunities.
RESULTS: The four key informants’ accounts were synthesized into a seven-phase preliminary workflow map that included: consult trigger, chart review, patient assessment, consideration of collaboration needs, resource search, referral or discharge, and post-discharge. Across the interviews, key informants described that transition planning involved substantial manual effort, reliance on informal clinician knowledge, diverse collaboration with other care providers, and limited visibility into post-discharge outcomes. Four recurring patterns were described: relationship-driven and locally embedded transition work; fragmented, outdated, and under-trusted information systems; largely invisible post-discharge outcomes; and conditional, mechanism-dependent trust in digital tools. Six preliminary considerations for future digital or AI-supported transition tools were identified: a trusted resource directory, wait-time and availability visibility, referral tracking, shared psychosocial and social work notes, a human provider directory, and efficient filtering of verified resources.
CONCLUSIONS: In this exploratory key-informant study, MHCs described ED-to-community care transitions as relying heavily on local knowledge, informal professional relationships, and manual workarounds. These preliminary clinician-reported observations identify considerations that may inform future co-design and implementation planning.
PMID:42975549 | DOI:10.2196/104833
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