Evidence map›Paper›PMID 41135594›Full record

ArticleJournal of the American Medical Directors Association2026

An Electronic Health Record Algorithm's Performance to Identify Cognitive Impairment in Primary Care.

Christine E Kistler, Molly E Lynch, Feng-Chang Lin, Yumei Yang, Winfred Frazier, Laura C Hanson

Abstract read
In one paragraph

Article in Journal of the American Medical Directors Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Christine E KistlerDivision of Geriatric Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA; Department of Family Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: Kistlerc@pitt.edu.
Molly E LynchCecil G. Sheps Center for Health Services Research, Chapel Hill, NC, USA.
Feng-Chang LinDepartment of Biostatistics, Gillings School of Global Public Health, Chapel Hill, NC, USA.
Yumei YangDepartment of Biostatistics, Gillings School of Global Public Health, Chapel Hill, NC, USA.
Winfred FrazierDepartment of Family and Community Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Laura C HansonCecil G. Sheps Center for Health Services Research, Chapel Hill, NC, USA; Division of Geriatric Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Palliative Care & Hospice Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Funding

Improving Primary Care Clinicians’ Advance Care Planning for Persons Living with Alzheimer’s Disease and Related DementiasR56AG075896 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KISTLER, CHRISTINE E · 2022 to 2023
$772k
NIA NIH HHS R56 AG075896
6 · The paper itself

Abstract

objectivesMost people with dementia are diagnosed and cared for in primary care. We examined the performance of an electronic health record (EHR) algorithm to identify older adults with dementia or mild cognitive impairment in primary care.

designRetrospective cohort study. SETTING AND

participantsWe identified all adults aged 65+ seen for a routine visit from December 2021 to December 2022 in 59 primary care clinics. We used representative sampling to create a cohort of 525 older adults.

methodsWe used trained research staff to confirm the presence of dementia or mild cognitive impairment via gold standard chart review. We then applied a list of 114 cognitive impairment-related Internaitonal Classification of Diseases, 10

resultsOf the representative cohort (n = 525), 59% were women and 81% were White, with an average age of 74.5 ± 6.9 years. Dementia or mild cognitive impairment was present on EHR review in 8.6% (n = 45) of the cohort. The algorithm had a specificity of 98.1%, a sensitivity of 60.0%, a positive predictive value of 0.75, and a negative predictive value of 0.96. The overall predictive performance (F-1 score) was 0.667. Nonspecific ICD-10 was the most common, with "unspecified dementia" representing 17 of 34 ICD-10 codes found by the algorithm. CONCLUSIONS AND IMPLICATIONS: Optimization of an EHR algorithm to detect cognitive impairment by combining cognitive impairment-related ICD-10 codes and dementia medications identified people with dementia or mild cognitive impairment in primary care with high specificity and acceptable sensitivity, although ICD-10 codes remained nonspecific.

Indexed as

AlgorithmsCognitive DysfunctionDementiaElectronic Health RecordsPrimary Health CareAgedAged, 80 and overFemaleHumansMaleRetrospective StudiesAlzheimer's disease and related dementiasCognitive impairmentprimary care

Identifiers

PMID41135594
PMCPMC13290007

What Socratic holds

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.