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Updated: Apr 12, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Psychiatric assessment and screening for the elderly in primary care: design, implementation, and preliminary results
Robert C Abrams1, Blanca Boné2, M Cary Reid2
1Department of Psychiatry and Division of Geriatrics and Palliative Medicine, Weill Cornell Medical College. Box 140, New York Presbyterian Hospital, 525 East 68 St., New York, NY 10065.
Introduction:
We describe the design and implementation of a psychiatric collaborative care model in a University-based geriatric primary care practice. Initial results of screening for anxiety and depression are reported.
Methods And Materials:
Screens for anxiety and depression were administered to practice patients. A mental health team, consisting of a psychiatrist, mental health nurse practitioner and social worker, identified patients who on review of screening and chart data warranted evaluation or treatment. Referrals for mental health interventions were directed to members of the mental health team, primary care physicians at the practice, or community providers.
Results:
Subjects (N=1505) comprised 38.2% of the 3940 unique patients seen at the practice during the 4-year study period. 37.1% (N=555) screened positive for depression, 26.9 % (N=405) for anxiety, and 322 (21.4%) screened positive for both. Any positive score was associated with age (p<0.033), female gender (p<0.006), and a non-significant trend toward living alone (p<0.095). 8.87% had suicidal thoughts.
Conclusions:
Screening captured the most affectively symptomatic patients, including those with suicidal ideation, for intervention. The partnering of mental health professionals and primary care physicians offers a workable model for addressing the scarcity of expertise in geriatric psychiatry.
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