JMIR Publications

ABSTRACT

Background:

Autoimmune diseases are heterogeneous, multisystem conditions that create significant challenges for diagnosis, monitoring, and research. The growing availability of electronic health records (EHRs), administrative databases, registries, and digital health technologies has expanded opportunities to study these diseases in real-world settings. However, the landscape of health information technology (health IT) and data sources used in autoimmune disease research remains poorly characterized.

Objective:

The objective of this systematic review was to examine and characterize trends in the use of health information technology and data sources in autoimmune disease research from 2009 to 2024.

Methods:

The systematic review searched PubMed and Web of Science for English-language studies published between January 1, 2009, and December 31, 2024. Studies were eligible if they examined or used health IT or digital data sources in autoimmune disease research or care. Studies were excluded if they did not focus on autoimmune diseases, lacked a relevant health IT or data component, or fell outside the specified publication period. Three reviewers independently screened titles, abstracts, and full texts using Covidence. Data was extracted and summarized on autoimmune conditions studied, health IT modalities, data sources, and study settings.

Results:

Fifty-five studies met the inclusion criteria. Lupus (including pediatric lupus) was the most frequently studied condition (approximately 25% of studies), followed by rheumatoid arthritis (18%), multiple sclerosis (9%), and diabetes mellitus (9%). Databases were the most common data source (51%), followed by EHRs (25%) and disease registries (18%). Most studies (84%) were conducted in research settings; only 16% took place in clinical environments. Geographic representation was concentrated in the United States and the United Kingdom. Emerging approaches, including patient-generated health data, digital health tools, repositories, and machine learning, were infrequently reported.

Conclusions:

Autoimmune disease research still relies heavily on traditional databases, EHRs, and registries. While these sources enable large-scale analyses, patient-centered digital data, advanced analytics, and underrepresented autoimmune conditions remain underutilized. Future work should prioritize interoperable data environments, more diverse disease coverage, and greater attention to equity and generalizability.