Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Currently accepted at: JMIR Infodemiology

Date Submitted: Mar 14, 2026
Date Accepted: Sep 22, 2026

This paper has been accepted and is currently in production.

It will appear shortly on 10.2196/93956

The final accepted version (not copyedited yet) is in this tab.

Online Health Communities for Rare Diseases: Cross-Sectional Content Analysis of Digital Service Provision and Artificial Intelligence Adoption

  • Umair ul Hassan; 
  • Muhammad Sohaib Ayub; 
  • Mona Isazad Mashinchi

ABSTRACT

Background:

Rare diseases affect roughly 10 % of the global population, yet each disorder is uncommon and patients frequently endure lengthy diagnostic journeys (5–7 years). Online Health Communities (OHCs) have become an essential source of peer support and disease‑specific information, yet their quality, safety, and accessibility remain poorly characterised, particularly for rare‑disease patients.

Objective:

To develop a multi‑dimensional assessment framework for rare‑disease OHCs and to apply it to a large sample of web‑based communities and Reddit subreddits, thereby identifying gaps in organisational structure, content quality, technical safeguards, and certification.

Methods:

A multi‑dimension framework (organisation, communication, content, certification, AI) was derived from the literature and primary data analysis. We identified 174 web-based OHCs maintained by patient support groups or organisations across the USA, UK, Australia, Canada and Ireland via the EURORDIS and Orphanet databases, and an additional 66 Reddit‑based OHCs. Descriptive statistics summarised the prevalence of each framework dimension across the sample.

Results:

In term of organisational profile, 99.4 % of the 174 OHCs were run by patient support groups or organisations, 97.7 % were registered charities, and 49.4 % operated as chapters of larger international bodies. Majority of sites addressed a single rare disease; 43.1 % hosted patient‑to‑patient forums, and 82.8 % enabled sharing of patient stories and testimonials. With respect to content quality, 93.1 % provided symptoms and pattern information; 64.9 % offered self‑help resources; only 9.8 % supplied socio‑legal advice. From technology specific dimensions, 60.9 % used HTTPS, 35.1 % employed basic analytics tools, and 17.8 % deployed AI features; none held HONCode certification. Among Reddit-based OHCs, 63.6 % consistently discussed disease symptoms and patterns; 30.3 % provided self‑help content. None were formally operated by support groups or patient organisations, and all lacked analytics tracking, AI integration, or health‑information certifications.

Conclusions:

The quality of information, digital sophistication, and adherence to established standards in rare‑disease OHCs are markedly insufficient. Only a minority of communities utilise AI, analytics, or recognised certification schemes, and a large proportion rely on informal or community‑driven platforms such as Reddit. These findings underscore the urgent need for organisational and technical support to enhance the safety, reliability, and accessibility of OHCs that provide critical support to patients and their families.


 Citation

Please cite as:

ul Hassan U, Ayub MS, Mashinchi MI

Online Health Communities for Rare Diseases: Cross-Sectional Content Analysis of Digital Service Provision and Artificial Intelligence Adoption

JMIR Infodemiology. 22/09/2026:93956 (forthcoming/in press)

DOI: 10.2196/93956

URL: https://preprints.jmir.org/preprint/93956

Download PDF


Request queued. Please wait while the file is being generated. It may take some time.

© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.