Machine learning to improve analysis of electronic health data on disability and health: an untapped opportunity for health inequities research

Journal article

Electronic Health Records (EHRs) are a leading source of epidemiological data, but often lack standardised disability information. This gap hampers our ability to analyse the full scope of health inequities faced by people with disabilities. Current approaches to identify disability within EHRs have limitations because of inadequate proxies for disability or issues linking data sources. Machine learning (ML) offer unprecedented opportunities to create disability markers within EHRs, such as through unsupervised learning to classify disability groups and Natural Language Processing to extract relevant information from clinical notes. These methods have the potential improve disability-disaggregated analyses within EHRs to uncover patterns and provide a more comprehensive understanding of healthcare pathways and outcomes for people with disabilities. Leveraging these approaches to improve disability data in EHRs is a critical step towards improving health inequities research, but emphasise the importance of adhering to ethical guidelines and validating these new approaches.

Authors: Rotenberg, S; Mesinovic, M; Saloniki, E-C; Chen, S; Raine, R

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Elsevier
• Journal: Disability and Health Journal
• Volume: 19
• Issue: 2
• Article number: 102017
• Publication date: 2025-12-18
• Acceptance date: 2025-12-05
• DOI: 10.1016/j.dhjo.2025.102017
• EISSN: 1876-7583
• ISSN: 1936-6574
• Language: English