PreCare: Designing AI Assistants for Advance Care Planning (ACP) to Enhance Personal Value Exploration, Patient Knowledge, and Decisional Confidence

• URL: http://arxiv.org/abs/2505.09115v1
• Date: Wed, 14 May 2025 03:53:35 GMT
• Title: PreCare: Designing AI Assistants for Advance Care Planning (ACP) to Enhance Personal Value Exploration, Patient Knowledge, and Decisional Confidence
• Abstract summary: 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.<n>A comparative evaluation showed that PreCare's AI assistants significantly improved exploration of personal values, knowledge, and decisional confidence, and was preferred by 92% of participants.
• Score: 5.458688374520092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• MedArena: Comparing LLMs for Medicine-in-the-Wild Clinician Preferences [50.71326426975699]
  We present MedArena, an interactive evaluation platform for medical large language models (LLMs)<n>Given a clinician-provided query, MedArena presents responses from two randomly selected models and asks the user to select the preferred response.<n>Out of 1571 preferences collected across 12 LLMs up to November 1, 2025, Gemini 2.0 Flash Thinking, Gemini 2.5 Pro, and GPT-4o were the top three models by Bradley-Terry rating.
  arXiv  Detail & Related papers  (2026-03-13T22:30:26Z)
• A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic [21.374164324051012]
  Articulate Medical Intelligence Explorer (AMIE)<n>100 adult patients completed an AMIE text-chat interaction up to 5 days before their appointment.<n>Human safety supervisors monitored all patient-AMIE interactions in real time.
  arXiv  Detail & Related papers  (2026-03-09T14:43:40Z)
• Words to Describe What I'm Feeling: Exploring the Potential of AI Agents for High Subjectivity Decisions in Advance Care Planning [7.273122595358839]
  The need for reliable support in Advance Care Planning (ACP) grows as populations age and caregiver networks shrink.<n>We built an experience prototype (acpagent) and asked 15 participants in 4 workshops to train it to be their personal proxy in ACP decisions.<n>Our findings argue for a potential new role of AI in ACP where agents act as personal advocates for individuals, building mutual intelligibility over time.
  arXiv  Detail & Related papers  (2025-12-12T04:39:34Z)
• AI-based Clinical Decision Support for Primary Care: A Real-World Study [1.2764851761863103]
  We evaluate the impact of large language model-based clinical decision support in live care.<n>We studied AI Consult, a tool that serves as a safety net for clinicians by identifying potential documentation and clinical decision-making errors.
  arXiv  Detail & Related papers  (2025-07-22T18:37:33Z)
• Towards physician-centered oversight of conversational diagnostic AI [40.583050959984995]
  Real-world assurance of patient safety means that providing individual diagnoses and treatment plans is considered a regulated activity by licensed professionals.<n>Inspired by this, we propose a framework for effective, asynchronous oversight of the Articulate Medical Intelligence Explorer (AMIE) AI system.
  arXiv  Detail & Related papers  (2025-07-21T15:54:36Z)
• Ψ-Arena: Interactive Assessment and Optimization of LLM-based Psychological Counselors with Tripartite Feedback [51.26493826461026]
  We propose Psi-Arena, an interactive framework for comprehensive assessment and optimization of large language models (LLMs)<n>Arena features realistic arena interactions that simulate real-world counseling through multi-stage dialogues with psychologically profiled NPC clients.<n>Experiments across eight state-of-the-art LLMs show significant performance variations in different real-world scenarios and evaluation perspectives.
  arXiv  Detail & Related papers  (2025-05-06T08:22:51Z)
• Explainable AI Enhances Glaucoma Referrals, Yet the Human-AI Team Still Falls Short of the AI Alone [6.740852152639975]
  We investigate how various AI explanations help providers distinguish between patients needing immediate or non-urgent specialist referrals. We built explainable AI algorithms to predict glaucoma surgery needs from routine eyecare data as a proxy for identifying high-risk patients. We incorporated intrinsic and post-hoc explainability and conducted an online study with optometrists to assess human-AI team performance.
  arXiv  Detail & Related papers  (2024-05-24T03:01:20Z)
• K-QA: A Real-World Medical Q&A Benchmark [12.636564634626422]
  We construct K-QA, a dataset containing 1,212 patient questions originating from real-world conversations held on K Health. We employ a panel of in-house physicians to answer and manually decompose a subset of K-QA into self-contained statements. We evaluate several state-of-the-art models, as well as the effect of in-context learning and medically-oriented augmented retrieval schemes.
  arXiv  Detail & Related papers  (2024-01-25T20:11:04Z)
• Bridging the Skills Gap: Evaluating an AI-Assisted Provider Platform to Support Care Providers with Empathetic Delivery of Protocolized Therapy [5.6317091933490495]
  Despite high prevalence and burden of mental health conditions, there is a global shortage of mental health providers. We developed AI-Assisted Provider Platform (A2P2), a text-based virtual therapy interface that includes a response feature. We studied providers with and without expertise in mental health treatment delivering a therapy session using the platform with (intervention) and without (control) AI-assistance features.
  arXiv  Detail & Related papers  (2024-01-08T02:23:17Z)
• RECAP-KG: Mining Knowledge Graphs from Raw GP Notes for Remote COVID-19 Assessment in Primary Care [45.43645878061283]
  We present a framework that performs knowledge graph construction from raw GP medical notes written during or after patient consultations. Our knowledge graphs include information about existing patient symptoms, their duration, and their severity. We apply our framework to consultation notes of COVID-19 patients in the UK.
  arXiv  Detail & Related papers  (2023-06-17T23:35:51Z)
• Retrieval-Augmented and Knowledge-Grounded Language Models for Faithful Clinical Medicine [68.7814360102644]
  We propose the Re$3$Writer method with retrieval-augmented generation and knowledge-grounded reasoning. We demonstrate the effectiveness of our method in generating patient discharge instructions.
  arXiv  Detail & Related papers  (2022-10-23T16:34:39Z)
• Optimal discharge of patients from intensive care via a data-driven policy learning framework [58.720142291102135]
  It is important that the patient discharge task addresses the nuanced trade-off between decreasing a patient's length of stay and the risk of readmission or even death following the discharge decision. This work introduces an end-to-end general framework for capturing this trade-off to recommend optimal discharge timing decisions. A data-driven approach is used to derive a parsimonious, discrete state space representation that captures a patient's physiological condition.
  arXiv  Detail & Related papers  (2021-12-17T04:39:33Z)
• COVID-Net Clinical ICU: Enhanced Prediction of ICU Admission for COVID-19 Patients via Explainability and Trust Quantification [71.80459780697956]
  We introduce COVID-Net Clinical ICU, a neural network for ICU admission prediction based on patient clinical data. The proposed COVID-Net Clinical ICU was built using a clinical dataset from Hospital Sirio-Libanes comprising of 1,925 COVID-19 patients. We conducted system-level insight discovery using a quantitative explainability strategy to study the decision-making impact of different clinical features.
  arXiv  Detail & Related papers  (2021-09-14T14:16:32Z)
• NUVA: A Naming Utterance Verifier for Aphasia Treatment [49.114436579008476]
  Assessment of speech performance using picture naming tasks is a key method for both diagnosis and monitoring of responses to treatment interventions by people with aphasia (PWA) Here we present NUVA, an utterance verification system incorporating a deep learning element that classifies 'correct' versus'incorrect' naming attempts from aphasic stroke patients. When tested on eight native British-English speaking PWA the system's performance accuracy ranged between 83.6% to 93.6%, with a 10-fold cross-validation mean of 89.5%.
  arXiv  Detail & Related papers  (2021-02-10T13:00:29Z)
• Opportunities of a Machine Learning-based Decision Support System for Stroke Rehabilitation Assessment [64.52563354823711]
  Rehabilitation assessment is critical to determine an adequate intervention for a patient. Current practices of assessment mainly rely on therapist's experience, and assessment is infrequently executed due to the limited availability of a therapist. We developed an intelligent decision support system that can identify salient features of assessment using reinforcement learning.
  arXiv  Detail & Related papers  (2020-02-27T17:04:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.