Why We Need Patients and Community at the Center of AI Health Communication Research
Clinical Snapshot
PICO Framework
| P — Population | Patients receiving hospital discharge communications and healthcare consumers engaging with AI-generated health information |
| I — Intervention | Patient-centered, community-engaged approaches to AI health communication research and tool development |
| C — Comparator | Current AI health communication research approaches with limited patient and community involvement |
| O — Outcomes | Enhanced equity, consumer engagement, and effectiveness of patient-facing AI health communication tools |
Bottom Line
This commentary identifies critical gaps in current AI health communication research, specifically the underrepresentation of equity considerations and meaningful consumer engagement. The authors argue that while early evidence shows promise for AI-generated patient information improving comprehension, research in this field must evolve to include diverse patient populations and community voices from the design phase. For Australian clinicians, this is particularly relevant given our established frameworks for consumer engagement in healthcare research and the increasing implementation of digital health tools. The recommendations align with NHMRC guidelines for consumer involvement and could strengthen the evidence base for AI tools being considered for implementation in Australian hospitals. Healthcare professionals should advocate for patient-centered evaluation of AI communication tools before widespread adoption, ensuring these technologies serve all patients effectively rather than potentially widening health communication disparities.
Key Findings
P Value: Not applicable - commentary piece
Effect Size: Not applicable - commentary piece
Primary Outcome: Identification of equity and consumer engagement gaps in AI health communication research
Nnt Or Sensitivity: Not applicable - commentary piece
Confidence Interval: Not applicable - commentary piece
Clinical Application
High feasibility for implementation in Australian research settings, particularly given established consumer engagement frameworks Highly relevant to Australian healthcare given strong consumer engagement traditions through organizations like Consumers Health Forum of Australia, NHMRC consumer involvement requirements, and existing health literacy initiatives in NSW and Victorian health services Healthcare researchers, clinicians involved in discharge planning, health communication specialists, and healthcare administrators implementing AI tools
Abstract
Holderried and colleagues tested whether artificial intelligence (AI)-generated, patient-centered information can help people understand what they need to do after being discharged from a hospital. Participants demonstrated stronger comprehension when they viewed the simplified, patient-centered information rather than a standard letter. This work adds to the available early-phase evidence of AI supporting hospital discharge communication. To meaningfully progress this area of research, we now need to carefully consider how to enhance the design and evaluation of patient-facing AI health communication tools. In this commentary, we argue that equity and consumer engagement remain underrepresented in studies on patient-facing AI health communication tools and describe possible approaches to address this issue.
References
- 1.Ayre, J., Shao, L., & Dunn, A. G. (2026). Why we need patients and community at the center of AI health communication research. Journal of Medical Internet Research. https://doi.org/10.1007/s11606-008-0833-4
Related Research
Journal of medical Internet research
Advancing Human-Centered AI in Clinical Decision Support: Sociocognitive Human-in-the-Loop Study in HIV Care
2 Aug 2026
Neurological research
Federated deep learning model for epilepsy seizure detection using electroencephalogram (EEG) signal
2 Aug 2026
Neural networks : the official journal of the International Neural Network Society
MFBD: Model-free backdoor defense based on vision-language pre-trained models
2 Aug 2026
This content is for educational purposes for healthcare professionals only and does not constitute clinical advice. Clinical decisions should be based on individual patient assessment, current guidelines, and appropriate specialist consultation. Editorial Standards · Privacy Policy · Terms of Service