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Updated: Jun 25, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
A comparison of dementia diagnoses and cognitive function measures in Medicare claims and the Minimum Data Set
Joshua D Niznik1,2,3,4, Jennifer L Lund5, Laura C Hanson1,2
1Division of Geriatric Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Background:
Gold standard dementia assessments are rarely available in large real-world datasets, leaving researchers to choose among methods with imperfect but acceptable accuracy to identify nursing home (NH) residents with dementia. In healthcare claims, options include claims-based diagnosis algorithms, diagnosis indicators, and cognitive function measures in the Minimum Data Set (MDS), but few studies have compared these. We evaluated the proportion of NH residents identified with possible dementia and concordance of these three.
Methods:
Using a 20% random sample of 2018-2019 Medicare beneficiaries, we identified MDS admission assessments for non-skilled NH stays among individuals with continuous enrollment in Medicare Parts A, B, and D. Dementia was identified using: (1) Chronic Conditions Warehouse (CCW) claims-based algorithm for Alzheimer's disease and non-Alzheimer's dementia; (2) MDS active diagnosis indicators for Alzheimer's disease and non-Alzheimer's dementias; and (3) the MDS Cognitive Function Scale (CFS) (at least mild cognitive impairment). We compared the proportion of admissions with evidence of possible dementia using each criterion and calculated the sensitivity, specificity, and agreement of the CCW claims definition and MDS indicators for identifying any impairment on the CFS.
Results:
Among 346,013 non-SNF NH admissions between 2018 and 2019, 57.2% met criteria for at least one definition (44.7% CFS, 40.7% CCW algorithm, 26.0% MDS indicators). The MDS CFS uniquely identified the greatest proportion with evidence of dementia. The CCW claims algorithm had 63.7% sensitivity and 78.1% specificity for identifying any cognitive impairment on the CFS. Active diagnosis indicators from the MDS had lower sensitivity (47.0%), but higher specificity (91.0%).
Conclusions:
Claims- and MDS-based methods for identifying NH residents with possible dementia have only partial overlap in the cohorts they identify, and neither is an obvious gold standard. Future studies should seek to determine whether additional functional assessments from the MDS or prescriptions can improve identification of possible dementia in this population.
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