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Artificial Intelligence-Mediated Discharge Document for Accessible Health Care (AIM-HEALTH): Protocol for a
Nicola Pelizzari1, Mattia Savardi1, Luca Arrigoni2
1Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Lombardy, Italy.
JMIR Research Protocols
|July 3, 2026
Summary
AIM-HEALTH uses AI to create patient-friendly discharge documents, improving understanding and self-management for those with limited health literacy (HL). This study assesses its feasibility, safety, and patient acceptance.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Patient Communication
Background:
- Hospital discharge reports (HDRs) often use complex language, hindering patient comprehension and self-management, especially for individuals with limited health literacy (HL).
- Effective post-discharge communication is crucial for continuity of care and patient outcomes.
Purpose of the Study:
- To develop and evaluate AIM-HEALTH (Artificial Intelligence-Mediated Discharge Document for Accessible Healthcare), an AI-powered supplementary discharge document (SDD).
- To tailor SDDs to patients' health literacy levels, enhancing understanding of hospital discharge report content.
- To assess the clinical validation and patient acceptability of AI-generated discharge summaries.
Main Methods:
- Prospective observational study enrolling 200 adult patients from nephrology and cardiology units.
- Assessment of patient health literacy (HL) combined with HDR data and discharge interviews.
- Generation of HL-tailored SDDs and clinical informational performance reports (CIPRs) using agentic AI and large language models.
- Clinician validation of SDDs using a QUEST-informed tool and patient assessment of SDD accessibility and usefulness.
- On-premise data processing with strict data protection safeguards.
Main Results:
- Expected results include technical feasibility, workflow indicators (completion and withholding rates), and clinician-rated quality of SDDs (accuracy, appropriateness, completeness, safety).
- Evaluation of the perceived utility of CIPRs in identifying HDR omissions and inconsistencies.
- Patient-reported outcomes on SDD accessibility, comprehensibility, usefulness, and engagement at follow-up.
- Analysis of associations between SDD readability scores and patient comprehension.
Conclusions:
- AIM-HEALTH aims to provide preliminary evidence on the feasibility, safety, and acceptability of integrating AI-generated, HL-adapted SDDs into clinical workflows.
- Findings will inform future studies on the role of AI tools in improving post-discharge communication, especially for patients with chronic conditions.
- Mandatory clinician validation and on-premise data protection are integral to the system's implementation.
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