Designing AI Tools for Clinical Care Teams to Support Serious Illness Conversations with Older Adults in the Emergency Department

May 30, 2025 Β· Declared Dead Β· πŸ› arXiv.org

πŸ‘» CAUSE OF DEATH: Ghosted
No code link whatsoever

"No code URL or promise found in abstract"

Evidence collected by the PWNC Scanner

Authors Menglin Zhao, Zhuorui Yong, Ruijia Guan, Kai-Wei Chang, Adrian Haimovich, Kei Ouchi, Timothy Bickmore, Bingsheng Yao, Dakuo Wang, Smit Desai arXiv ID 2506.00241 Category cs.HC: Human-Computer Interaction Cross-listed cs.AI Citations 1 Venue arXiv.org Last Checked 4 months ago
Abstract
Serious illness conversations (SICs), discussions between clinical care teams and patients with serious, life-limiting illnesses about their values, goals, and care preferences, are critical for patient-centered care. Without these conversations, patients often receive aggressive interventions that may not align with their goals. Clinical care teams face significant barriers when conducting serious illness conversations with older adult patients in Emergency Department (ED) settings, where most older adult patients lack documented treatment goals. To understand current practices and identify AI support opportunities, we conducted interviews with two domain experts and nine ED clinical care team members. Through thematic analysis, we characterized a four-phase serious illness conversation workflow (identification, preparation, conduction, documentation) and identified key needs and challenges at each stage. Clinical care teams struggle with fragmented EHR data access, time constraints, emotional preparation demands, and documentation burdens. While participants expressed interest in AI tools for information synthesis, conversational support, and automated documentation, they emphasized preserving human connection and clinical autonomy. We present design guidelines for AI tools supporting SIC workflows that fit within existing clinical practices. This work contributes empirical understanding of ED-based serious illness conversations and provides design considerations for AI in high-stakes clinical environments.
Community shame:
Not yet rated
Community Contributions

Found the code? Know the venue? Think something is wrong? Let us know!

πŸ“œ Similar Papers

In the same crypt β€” Human-Computer Interaction

Died the same way β€” πŸ‘» Ghosted