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Accepted for/Published in: JMIR Research Protocols

Date Submitted: Mar 20, 2026
Date Accepted: May 29, 2026

The final, peer-reviewed published version of this preprint can be found here:

Artificial Intelligence–Mediated Discharge Document for Accessible Health Care (AIM-HEALTH): Protocol for a Prospective, Observational, Noninterventional Study

Pelizzari N, Savardi M, Arrigoni L, Gregori M, Conti A, Tedesco M, Tratta E, Ceretti E, Covolo L, Visconti M, Adamo M, Roccaro A, Vizzardi E, Signoroni A, Alberici F, Lombardi CM, Gelatti U

Artificial Intelligence–Mediated Discharge Document for Accessible Health Care (AIM-HEALTH): Protocol for a Prospective, Observational, Noninterventional Study

JMIR Res Protoc 2026;15:e95782

DOI: 10.2196/95782

PMID: 42398931

AIM-HEALTH: Artificial Intelligence-mediated Discharge Document for Accessible Healthcare - A study protocol

  • Nicola Pelizzari; 
  • Mattia Savardi; 
  • Luca Arrigoni; 
  • Marco Gregori; 
  • Adelaide Conti; 
  • Martina Tedesco; 
  • Elena Tratta; 
  • Elisabetta Ceretti; 
  • Loredana Covolo; 
  • Maria Visconti; 
  • Marianna Adamo; 
  • Aldo Roccaro; 
  • Enrico Vizzardi; 
  • Alberto Signoroni; 
  • Federico Alberici; 
  • Carlo Mario Lombardi; 
  • Umberto Gelatti

ABSTRACT

Background:

Hospital discharge reports (HDR) support continuity of care, yet their specialised terminology may hinder patient understanding and post-discharge self-management, particularly among individuals with limited health literacy (HL).

Objective:

AIM-HEALTH aims to develop and evaluate a clinician-validated, AI-powered supplementary discharge document (SDD) to support comprehension of HDR content, tailored to patients’ HL levels.

Methods:

This prospective, observational, non-interventional study will enrol 200 adults from the nephrology and cardiology units at ASST Spedali Civili hospital. Following written informed consent, participants’ HL will be assessed and combined with HDR structured data and an audio-recorded discharge interview to generate two outputs using locally deployed agentic AIs powered by open-weight LLM: (i) an HL-tailored SDD for patient use and (ii) a clinical performance report (CPR) highlighting omissions and inconsistencies between the HDR and the discharge interview to support clinical safety. Clinicians will validate the SDD using a QUEST-informed tool assessing accuracy, completeness, clarity, utility, and safety domains. All outputs will undergo clinical assessment; only suitable outputs will be retained, while corrections and unsuitable outputs will be used to iteratively refine the system. Patients will assess SDD perceived accessibility, comprehensibility, usefulness, and engagement. Data processing follows on-premise, DPIA-defined safeguards (data minimisation, pseudonymisation, security/incident management).

Results:

Expected results include: (i) technical feasibility and workflow indicators (completion and SDD withholding rates); (ii) clinician-rated accuracy, appropriateness, completeness, and safety of SDDs; (iii) perceived utility of the CPR for identifying omissions and inconsistencies between HDRs and discharge interviews; (iv) patient-reported accessibility, comprehensibility, usefulness, and engagement at ~1-month follow-up; and (v) associations between SDD readability index scores and perceived comprehensibility.

Conclusions:

AIM-HEALTH aims to streamline discharge workflow, improve post-discharge comprehension and safety, particularly for chronic conditions, and reduce avoidable readmissions linked to misunderstanding, under on-premise data protection and clinician validation. AIM-HEALTH is supported by Fondazione Cariplo.


 Citation

Please cite as:

Pelizzari N, Savardi M, Arrigoni L, Gregori M, Conti A, Tedesco M, Tratta E, Ceretti E, Covolo L, Visconti M, Adamo M, Roccaro A, Vizzardi E, Signoroni A, Alberici F, Lombardi CM, Gelatti U

Artificial Intelligence–Mediated Discharge Document for Accessible Health Care (AIM-HEALTH): Protocol for a Prospective, Observational, Noninterventional Study

JMIR Res Protoc 2026;15:e95782

DOI: 10.2196/95782

PMID: 42398931

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