Observational studyJMIR research protocols2026
Artificial Intelligence-Mediated Discharge Document for Accessible Health Care (AIM-HEALTH): Protocol for a Prospective, Observational, Noninterventional Study.
Observational study in JMIR research protocols, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHospital discharge reports (HDRs) support continuity of care; yet, their specialized terminology may hinder patient understanding and postdischarge self-management, particularly among individuals with limited health literacy (HL).
objectiveAIM-HEALTH (Artificial Intelligence-Mediated Discharge Document for Accessible Healthcare) aims to develop and evaluate a clinician-validated, artificial intelligence (AI)-powered supplementary discharge document (SDD) to support comprehension of HDR content, tailored to patients' HL levels.
methodsThis prospective, observational, noninterventional study will enroll 200 adults from the nephrology and cardiology units at Azienda Socio-Sanitaria Territoriale 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 an open-weight large language model: (1) an HL-tailored SDD for patient use and (2) a clinical informational performance report (CIPR) highlighting omissions and inconsistencies between the HDR and the discharge interview to support clinical safety. Clinicians will validate the SDD using a QUEST (quality, understanding, expression style, safety, and trust)-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, Data Protection Impact Assessment-defined safeguards (data minimization, pseudonymization, and security or incident management).
resultsExpected results include (1) technical feasibility and workflow indicators (completion and SDD withholding rates); (2) clinician-rated accuracy, appropriateness, completeness, and safety of SDDs; (3) perceived utility of the clinical informational performance report for identifying omissions and inconsistencies between HDRs and discharge interviews; (4) patient-reported accessibility, comprehensibility, usefulness, and engagement at ~1-month follow-up; and (5) associations between SDD readability index scores and perceived comprehensibility. This study is approved by the local institutional review board (NP 6761-62, February 24, 2026). Data collection commenced on May 29, 2026.
conclusionsAIM-HEALTH is intended to generate preliminary feasibility, safety, and acceptability evidence on the integration of an AI-generated, HL-adapted SDD into routine workflows, particularly for patients with chronic conditions. The findings are intended to inform the design of future comparative studies addressing the potential role of such tools in postdischarge communication, under on-premise data protection and mandatory clinician validation. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/95782.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.