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Updated: Oct 27, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Mild Cognitive Impairment Identification in Geriatric and Primary Care Clinics and Driving Assessment Outcomes
Kourosh A Moshiri1,2, Mahon Mahmodian1
1Geriatrics, Eisenhower Medical Center, Rancho Mirage, USA.
Background:
Mild cognitive impairment (MCI) can affect complex daily activities, including driving. This quality-improvement project compared documented MCI case counts in an outpatient geriatric clinic and a traditional primary care clinic and described Department of Motor Vehicles (DMV) referral documentation and driving assessment outcomes.
Materials And Methods:
A retrospective Epic electronic health record (EHR) review (Epic Systems Corporation, Verona, WI, USA) covered 2021-2023 and adults aged 65 years and older. Electronic G31.84 queries identified candidates; preserved basic and instrumental daily function and verified Montreal Cognitive Assessment (MoCA) scores of 19-25 were required for inclusion. Scanned MoCA tests and both DMV forms were located by manual chart review. Completed written and behind-the-wheel testing and overall pass/fail outcomes were distinguished. Counts and proportions were summarized descriptively, with exact binomial 95% confidence intervals (CIs) for assessment outcomes.
Results:
Clinic totals were 3,233 geriatric and 3,943 primary care visits. The geriatric query identified 60 candidates. Scanned MoCA tests were located in 36; 24 without located tests were excluded. Two scores were 11, leaving 34 eligible patients. The primary care code query returned zero patients. All 34 were offered DMV assessment through shared decision making; 17 (50.0%) had documented referrals with DMV notification and initial reporting forms in their charts. The later provider form was documented for 10 of the 17. Eight completed testing (47.1% of referred patients; 80.0% of those with the later form documented). Two passed (25.0%; 95% CI, 3.2%-65.1%) and six failed (75.0%; 95% CI, 34.9%-96.8%). Eight patients continued driving without a documented referral; the reasons were undocumented.
Conclusions:
Documented MCI case capture differed between clinics. Findings among assessment completers support routine clinical review of driving safety, individualized referral for formal evaluation, and coordinated follow-up. The failure proportion from this small, selected subgroup cannot be generalized to the wider MCI population. Prospective evaluation should examine patient safety, experience, and continued mobility.
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