2020 Fall GAPNA Newsletter Volume 39 Issue 3

Electronic Health Records-Based Tool Uses Data to Detect Undiagnosed Dementia

Under-recognition of dementia is a major concern in the aging population. But little work has been done to translate findings from models of future risk of dementia into systems that could be used in primary care settings to detect undiagnosed cases. A newly developed tool based on data in electronic health records (EHRs) could be used to detect patients with undiagnosed dementia and flag their records for future follow-up. The model could help address the problem of missed or late diagnoses of dementia in older adults.

To develop the predictive tool — dubbed the EHR Risk of Alzheimer’s and Dementia Assessment Rule (eRADAR) — researchers selected and validated 31 markers in EHRs that were associated with a higher likelihood of dementia and therefore could be used to help detect patients who might be underdiagnosed. Researchers first analyzed the records of research participants who had been classified as having no dementia, recognized dementia, or unrecognized dementia during their study visit.

Participants were classified as having dementia if the EHR included diagnosis codes for dementia, memory complaints or dementia medication during the 2 years before the visit date, and as unrecognized if none of these criteria were met.

Out of a sample of 4,330 patients across 16,665 visits, researchers found 1,015 visits resulted in a diagnosis of dementia. Of these diagnoses, 498 (49%) had not already been coded as such in EHR records. Researchers then looked at a variety of markers by diagnosis to identify which ones were key predictors of undiagnosed dementia and to create the eRADAR model, which provides a score that increases with the likelihood an individual has dementia.

These markers included demographic data such as age and sex, dementia-related symptoms such as psychosis, antidepressant prescriptions, emergency department visits, and health conditions such as cerebrovascular disease and diabetes. Individuals who had eRADAR scores in the top 5% were more than five times as likely than the patient population as a whole to have unrecognized dementia, suggesting that it would be important to screen more individuals with high eRADAR scores.

This study demonstrated that a tool such as eRADAR, which uses readily available EHR data, could accurately detect individuals who should be screened for dementia. These tools could have several benefits. Patients could access appropriate care early on and have more time to participate in legal, financial, and long-term care planning.

Researchers also noted that earlier diagnosis could facilitate use of evidence-based care models, enabling better symptom management for patients.


Barnes, D.E., Zhou, J., Walker, R.L., Larson, E.B., Lee, S.J., Boscardin, W.J., … Dublin, S. et al. Development and validation of eRADAR: A tool using EHR data to detect unrecognized dementia. Journal of the American Geriatrics Society, 68(1), 103-111.