The characteristics and performance of health data domains in supporting dementia identification: A systematic review

Abstract Electronic health records (EHRs) offer growing potential for dementia identification, yet a synthesis of how specific data domains contribute to accurate detection is lacking. This systematic review assessed the role and performance of EHR‐derived data in identifying dementia. Six databases were searched up to April 2025. Fifty studies met inclusion criteria, examining routinely collected health data across care settings. Ten key domains supported dementia identification, including demographics, diagnoses, medications, symptoms, and structured assessments. Most data were sourced from primary care EHRs (48%), with studies primarily conducted in the United Kingdom and United States. Case–control and retrospective cohort designs were commonly applied, often using logistic regression. Cognitive and behavioral domains contributed most to specificity (57.5%–99.9%). Alzheimer's‐specific models had higher accuracy than general dementia models (mean accuracy: 74.6 vs. 67.1). Integrating diverse EHR data, especially cognitive and symptomatic variables, can improve dementia detection. Future research should focus on model validation, standardization, and clinical implementation.