PreCare: Designing AI Assistants for Advance Care Planning (ACP) to Enhance Personal Value Exploration, Patient Knowledge, and Decisional Confidence
This work addresses the need for more personalized and effective online tools for individuals engaging in end-of-life care planning, though it appears incremental by building on existing ACP frameworks.
The paper tackled the gap in online Advance Care Planning (ACP) by designing PreCare, an AI-driven website with three assistants, which achieved an excellent System Usability Scale rating and significantly improved personal value exploration, knowledge, and decisional confidence, with 92% user preference in evaluations.
Advance Care Planning (ACP) allows individuals to specify their preferred end-of-life life-sustaining treatments before they become incapacitated by injury or terminal illness (e.g., coma, cancer, dementia). While online ACP offers high accessibility, it lacks key benefits of clinical consultations, including personalized value exploration, immediate clarification of decision consequences. To bridge this gap, we conducted two formative studies: 1) shadowed and interviewed 3 ACP teams consisting of physicians, nurses, and social workers (18 patients total), and 2) interviewed 14 users of ACP websites. Building on these insights, we designed PreCare in collaboration with 6 ACP professionals. PreCare is a website with 3 AI-driven assistants designed to guide users through exploring personal values, gaining ACP knowledge, and supporting informed decision-making. A usability study (n=12) showed that PreCare achieved a System Usability Scale (SUS) rating of excellent. A comparative evaluation (n=12) showed that PreCare's AI assistants significantly improved exploration of personal values, knowledge, and decisional confidence, and was preferred by 92% of participants.