Assigning Probable Dementia Status Using Routinely Collected Electronic Health Record Data

More than half of older adults with Alzheimer's Disease and Related Dementias (ADRD) are undiagnosed, limiting timely access to person-centered care. Therefore, clinicians, researchers, and population health managers need scalable, reproducible approaches to monitor both prevalence and diagnostic gaps. We evaluated whether a decision-analytic modeling framework can translate a limited number of clinician-adjudicated cases of ADRD into a probabilistic computational phenotype for accurate, population-level assignments of probable ADRD in the emergency department (ED) setting using routinely collected electronic health record (EHR) data.

Retrospective cohort study of 5000 adults aged ≥ 65 years from nine EDs within a large integrated health system (2014-2022). We randomly selected 500 individuals for clinician adjudication of dementia status (reference cohort), reserving the remaining 4500 as a phenotyping cohort. We developed the phenotype as a logistic regression model trained on adjudicated cases, embedding pattern-mixture multiple imputation to address information bias. We applied decision-curve analysis to evaluate clinical utility across probabilistic thresholds. We applied the phenotype to assign dementia status to 4500 unadjudicated patients and compared clinical characteristics to adjudicated cases.

The mean (SD) age was 77.4 (9.0) years; 55.4% were women; 102 individuals (20.4%) had clinician-adjudicated ADRD. The model demonstrated good discrimination (AUROC 0.87; 95% CI 0.82-0.91). Decision-curve analysis revealed net clinical benefit across examined thresholds (predicted probabilities 12%-32%), identifying an additional 16-18 probable ADRD cases per 100 older adults. Among those without ADRD-related diagnosis codes, net benefit ranged from 8 to 13 additional correct identifications per 100. Phenotype-assigned cases closely resembled clinician-adjudicated cases (standardized mean differences ≤ 0.20).

A probabilistic computational phenotype derived from routinely collected EHR data accurately reproduced clinician-adjudicated ADRD status and demonstrated net clinical benefit, including among ED patients whose ADRD was not captured by diagnosis codes. Adoption of this replicable framework may enable healthcare organizations to strengthen ADRD surveillance and reduce underdiagnosis.