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Updated: Oct 4, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Lessons from Detecting Cognitive Impairment Including Dementia (DetectCID) in Primary Care
Alissa Bernstein Sideman1,2,3,4, Rachel Chalmer5, Emmeline Ayers6
1Philip R. Lee Institute for Health Policy Studies, University of California, San Francisco, San Francisco, CA, USA.
Background:
Cognitive impairment, including dementia, is frequently under-detected in primary care. The Consortium for Detecting Cognitive Impairment, including Dementia (DetectCID) convenes three multidisciplinary teams that are testing novel paradigms to improve the frequency and quality of patient evaluations for detecting cognitive impairment in primary care and appropriate follow-up.
Objective:
Our objective was to characterize the three paradigms, including similarities and differences, and to identify common key lessons from implementation.
Methods:
A qualitative evaluation study with dementia specialists who were implementing the detection paradigms. Data was analyzed using content analysis.
Results:
We identified core components of each paradigm. Key lessons emphasized the importance of engaging primary care teams, enabling primary care providers to diagnose cognitive disorders and provide ongoing care support, integrating with the electronic health record, and ensuring that paradigms address the needs of diverse populations.
Conclusion:
Approaches are needed that address the arc of care from identifying a concern to post-diagnostic management, are efficient and adaptable to primary care workflows, and address a diverse aging population. Our work highlights approaches to partnering with primary care that could be useful across specialties and paves the way for developing future paradigms that improve differential diagnosis of symptomatic cognitive impairment, identifying not only its presence but also its specific syndrome or etiology.
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