Personalizing Prostate Cancer Education for Patients Using an EHR-Integrated LLM Agent
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Abstract (arXiv)
Cancer patients often lack timely education and personalized support due to clinician workload. This quality improvement study develops and evaluates a Large Language Model (LLM) agent, MedEduChat, which is integrated with the clinic's electronic health records (EHR) and designed to enhance prostate cancer patient education. Fifteen non-metastatic prostate cancer patients and three clinicians recruited from the Mayo Clinic interacted with the agent between May 2024 and April 2025. Findings showed that MedEduChat has a high usability score (UMUX 83.7 out of 100) and improves patients' health confidence (Health Confidence Score rose from 9.9 to 13.9). Clinicians evaluated the patient-chat interaction history and rated MedEduChat as highly correct (2.9 out of 3), complete (2.7 out of 3), and safe (2.7 out of 3), with moderate personalization (2.3 out of 3). This study highlights the potential of LLM agents to improve patient engagement and health education.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Sep 2024.
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Citing papers per year: 2025: 2; 2026: 5.
7 papers.
- 2025MedOmni-45deg: A Safety-Performance Benchmark for Reasoning-Oriented LLMs in Medicinecited by 6
- 2026MatrAIx: Simulating the World with 8.3 Billion Persona Agentscited by 5
- 2026Automating SKILL.md Generation for Computer-Using Agents via Interaction Trajectory Miningcited by 2
- 2025Towards Better Health Conversations: The Benefits of Context-seekingcited by 0
- 2026The Daily Dose: Workflow-Integrated Large Language Model Automation for Clinical Summarization and Trial Identification in Radiation Oncologycited by 0
- 2026Improving Heart-Focused Medical Question Answering in LLMs via Variance-Aware Rubric Rewards with GRPOcited by 0
- 2026Exploration of Foundation Model-Based Robots in Patient and Elderly Carecited by 0
- 2026MatrAIx: Simulating the World with 8.3 Billion Persona Agents4 Aug 2026
- 2026Automating SKILL.md Generation for Computer-Using Agents via Interaction Trajectory Mining18 Jun 2026
- 2026Exploration of Foundation Model-Based Robots in Patient and Elderly Care8 Jun 2026
- 2026The Daily Dose: Workflow-Integrated Large Language Model Automation for Clinical Summarization and Trial Identification in Radiation Oncology25 May 2026
- 2026Improving Heart-Focused Medical Question Answering in LLMs via Variance-Aware Rubric Rewards with GRPO17 Apr 2026
- 2025Towards Better Health Conversations: The Benefits of Context-seeking14 Sep 2025
- 2025MedOmni-45deg: A Safety-Performance Benchmark for Reasoning-Oriented LLMs in Medicine22 Aug 2025
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- 2025Dr. GPT Will See You Now, but Should It? Exploring the Benefits and Harms of Large Language Models in Medical Diagnosis using Crowdsourced Clinical Cases
- 2026Are LLMs Ready to Assist Physicians? PhysAssistBench for Interactive Doctor-Patient-EHR Assistance
- 2026MedArena: Comparing LLMs for Medicine-in-the-Wild Clinician Preferences
- 2025Lessons Learned from Evaluation of LLM based Multi-agents in Safer Therapy Recommendation
- 2025OpenAIs HealthBench in Action: Evaluating an LLM-Based Medical Assistant on Realistic Clinical Queries
- 2024Answering real-world clinical questions using large language model based systems
- 2026Medical Knowledge Simplification for Patients in the Era of LLMs: A Case Study on Diabetes
- 2024Agentic LLM Workflows for Generating Patient-Friendly Medical Reports