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Updated: Jun 14, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Increasing Identification and Follow-Up of Cognitive Impairment in Primary Care: A Quality Improvement Pilot Study
1Patricia A. Chin School of Nursing, California State University, Los Angeles, CA, USA.
Introduction:
Beyond our population growing older and living longer, there is an increased risk of developing a cognitive disorder. Standardized screening during a routine visit in primary care may be ideal for early detection of mild cognitive impairment (MCI) and follow-up for cognitive changes.
Aim:
This quality improvement (QI) project aimed to determine the impact of implementing the Mini-Cog© quick screening for early dementia detection to identify and follow up on the cognitive impairment of older adults in a primary care clinic setting.
Methods:
Implementation started in February 2024 in a primary care clinic in Southern California. Data was collected for this project over a total of 16 weeks. This QI project implemented a routine cognitive screening using the Mini-Cog©. Cognitive impairment was identified, and if indicated by the Mini-Cog© scores, follow-up for a cognitive assessment and care plan services were initiated. Data were obtained from the project site's electronic medical record on a total sample size of 471 participants (n = 382 in the pre-implementation group and n = 89 in the post-implementation group).
Results:
Pearson's chi-square test indicated a statistically significant improvement in the identification rate of cognitive impairment, increasing from 11.8% (n = 45 out of 382) at pre-implementation to 34.8% (n = 31 out of 89) at post-implementation, and specifically, mild cognitive impairment increased from zero identified in pre-implementation to 12.4% (n = 11 out of 89) post-implementation. Lastly, follow-up rates improved from 91.1% (n = 41 out of 45) to 100% (n = 31 out of 31) in post-implementation, and clinical significance was evident based on the phi-coefficient (φ = 0.196), indicating a small effect size and a 100% follow-up rate.
Conclusions:
The findings of this project suggest older adults should receive cognitive screenings to help identify early cognitive impairment and increase follow-up for further evaluation, treatment, and advanced care planning.
