Passive Digital Marker-enabled identification and engagement for Alzheimer's disease and related dementias care in primary care: the INCLUDE study protocol.

Authors

Diana Summanwar, Leonard Lee, Deanna R Willis, Zina Ben Miled, Christina Baucco, Emily C C Webber, Anthony J Perkins, April Y Powell, Nicole R Fowler

Published in

BMJ open. Volume 16. Issue 9. Pages e116755. Sep 08, 2026. Epub Sep 08, 2026.

Abstract

More than half of older adults living with Alzheimer's disease and related dementias (ADRD) never receive a formal diagnosis, and when a diagnosis occurs, it is often years after symptom onset. Primary care clinicians are ideally positioned to detect ADRD early; however, current workflows lack scalable tools that support systematic identification and follow-up. The Passive Digital Marker (PDM), a machine learning model that uses structured electronic health record (EHR) data, can identify patients at elevated risk for ADRD without adding burden to clinicians. This protocol outlines a feasibility study to develop and evaluate a patient-informed secure messaging intervention paired with PDM-based risk stratification to enhance patient engagement in cognitive assessment in primary care settings.
This will be a non-randomised pilot study conducted across 12 single health system primary care clinics. The PDM will be applied to EHR data to identify patients aged ≥65 years who are at high risk for ADRD. High-risk patients will receive a co-designed secure message prior to and after upcoming primary care visits encouraging follow-up evaluation with a trained nurse, the Brain Health Navigator (BHN). The primary objectives are to: (1) determine the feasibility of applying the PDM to EHR data across 12 primary care clinics; (2) assess the feasibility of engaging patients identified as positive on the PDM through secure text messaging prior to a primary care encounter and (3) evaluate engagement with the BHN following secure text messaging. Study outcomes will assess the feasibility of implementing the PDM and secure messaging workflow, including identification of high-risk patients using the PDM, message delivery and patient engagement measured through message open rates, completion of cognitive concern questions and appointments scheduled with the BHN. Quantitative data will be analysed using descriptive statistics.
This study was deemed exempt as part of enhanced patient care. The findings will be disseminated through peer-reviewed publications, professional conferences, health system reports and public-facing communications.
NCT07016178.

PMID:
42711082
Bibliographic data and abstract were imported from PubMed on 09 Sep 2026.

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