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Updated: Sep 17, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Enhancing early detection of cognitive impairment in primary care with the TabCAT-BHA
Andrew G Breithaupt1,2, Alissa Bernstein Sideman3,4,5, Collette Goode1
1Memory and Aging Center, Department of Neurology, University of California, San Francisco, California, USA.
Introduction:
As dementia cases increase and new therapies become available, timely diagnosis is critical yet challenging in primary care. We evaluated the TabCAT-Brain Health Assessment (TabCAT-BHA) digital paradigm to assist with early detection and diagnosis.
Methods:
This implementation study involved 21 primary care providers (PCPs) serving 2733 eligible patients in a family medicine clinic. PCPs initiated cognitive screening using the TabCAT-BHA, administered by medical assistants, with results integrated into the electronic health record. The study assessed changes in PCP cognitive diagnoses, specialist referrals, and implementation metrics.
Results:
TabCAT-BHA was adopted by 95% of PCPs, who were more likely to diagnose cognitive disorders (adjusted odds ratio: 1.72, 95% confidence interval: 1.13-2.62, p = 0.01). Practice-wide diagnosis rates remained elevated after research support ended. PCPs reported increased confidence, reduced stress in diagnosing dementia, and preferred TabCAT-BHA over traditional assessments.
Discussion:
TabCAT-BHA was well accepted and sustained, offering a scalable solution for cognitive impairment diagnosis in primary care.
Highlights:
A digital test paradigm increased cognitive impairment diagnoses in primary care. Increased diagnoses across minority groups may mitigate healthcare disparities. Clinicians preferred the digital cognitive assessment over alternative tests. The paradigm decreased clinician stress and increased confidence around diagnosis. Integration with clinic staff workflow yielded high adoption and sustainability.
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