Streamlining eligibility assessment for Alzheimer's disease-modifying therapies: Prediction of MMSE scores using the digital clock and recall

Introduction The eligibility of anti-amyloid disease-modifying therapies (DMTs) and their integration into clinical practice in some institutions requires a specific range of Mini-Mental State Examination (MMSE) scores. Reliance on this pencil-and-paper psychometric instrument imposes operational burdens and risks of perpetuating health disparities, given the test's known educational and cultural biases. This study evaluates the efficacy of the Digital Clock and Recall (DCR™)—a rapid, FDA-listed digital cognitive assessment—to crosswalk to MMSE scores using machine learning, thereby offering a faster, scalable, and equitable mechanism for patient triage. Methods We conducted a retrospective analysis using data from the multi-site Bio-Hermes-001 (BH) study ( N CT04733989, N = 945). Participants were clinically classified as cognitively unimpaired, mild cognitive impairment, or probable Alzheimer's dementia. We trained a Poisson elastic net regression model on 70% of the sample, using age and multimodal digital features derived from the DCR (including drawing kinematics and voice acoustics) to predict MMSE scores. The model was validated using the remaining 30% of Bio-Hermes-001 and an independent external validation cohort from the Apheleia study (NCT05364307, N = 238). Results The machine learning model predicted MMSE scores with a root-mean-squared error (RMSE) of 2.43 in the BH test set. This error margin falls within the established test-retest reliability range of the manual MMSE itself (∼4.0–4.2 points at short inter-test intervals), providing evidence that the predicted score is of comparable precision to a repeat human administration of the MMSE. External validation in the Apheleia cohort demonstrated robust generalizability (RMSE = 2.62). In the BH held-out test set, the model showed comparable performance across Race (White RMSE = 2.46; Non-White RMSE = 2.25) and Ethnicity (Hispanic RMSE = 2.19; Non-Hispanic RMSE = 2.45), a balanced pattern also observed in the Apheleia-001 external cohort. Exploratory demographic analyses on prediction errors, including Age, Sex, Race, and Ethnicity, yielded significant differences only for Sex and Age in Apheleia, with signed errors becoming progressively more negative (i.e., increasing under-prediction) at older ages for the latter. This scarcity of statistical differences across cohorts suggested that our predictions were fair. Discussion Machine learning can leverage multimodal features from the DCR to accurately and equitably crosswalk to MMSE scores in support of current guidelines, transforming a time-intensive manual test into a rapid, automated assessment. By deploying this “digital triage” engine, where traditional assessments are still used for DMT eligibility, healthcare systems can streamline the identification of DMT-eligible patients, reduce specialist referral bottlenecks, and ensure that access to life-altering therapies is determined by pathology rather than demography.