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Updated: Jan 18, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
A Large Health System Quality Improvement Intervention Providing Training and Tools to Improve Detection of Cognitive
Barak Gaster1, Monica Zigman Suchsland2, Joshua M Liao1,3
1Department of Medicine, University of Washington, Seattle, Washington, USA.
Background:
Primary care providers (PCPs) are at the forefront of evaluating cognitive concerns and detecting Alzheimer's disease and related dementias (ADRD), but they generally lack the training and tools to do so.
Methods:
We performed a 2-year pragmatic intervention across a large health system of 14 community-based primary care clinics (94 PCPs). The intervention consisted of an education series integrated with workup tools in the exam room to assist PCPs in evaluating cognition. Electronic health record (EHR) data was extracted for 9 months before and 9 months after the intervention. Outcome measures were the number of cognitive assessments that PCPs recorded as discrete results in the EHR and the number of patients who PCPs newly diagnosed with an ADRD-related diagnosis.
Results:
Comparing EHR data from the 9 months before the intervention to the 9 months after the intervention, the number of cognitive assessments documented in the EHR increased from 2.8 per month to 19.8 per month (p < 0.001), and the number of new ADRD-related diagnoses made by PCPs increased from 6.2 per month to 14.6 per month (p = 0.012).
Conclusions:
An intervention integrating tools for PCPs to use in the exam room, together with concise continuing education, increased the number of cognitive evaluations and the number of ADRD-related diagnoses in a large primary care health system. Such interventions are essential for building age-friendly ambulatory health systems and connecting patients to improved and innovative models of ADRD care.
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