Evidence map›Paper›PMID 40073397›Full record

ArticleJMIR formative research2025

Digital Assessment of Cognitive Health in Outpatient Primary Care: Usability Study.

Adam J Doerr, Taylor A Orwig, Matthew McNulty, Stephanie Denise M Sison, David R Paquette, Robert Leung, Huitong Ding, Stephen B Erban, Bruce R Weinstein, Yurima Guilarte-Walker and 5 more

Abstract read
In one paragraph

Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Trial
  2. Pharmacy (Basel, Switzerland) · 2026
    Article
  3. Article
  4. Classifying cognitive status with TabCAT Match and UDS executive function tests.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Article
  5. Article
  6. Article
  7. Review
  8. Article
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

15 authors.

Adam J Doerr *Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0009-0006-4611-7919
Taylor A Orwig *Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0000-0002-4899-7677
Matthew McNultyProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0009-0007-1061-0901
Stephanie Denise M SisonDivision of General Internal Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0000-0003-3894-7219
David R PaquetteMassachusetts AI and Technology Center, Manning College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, United States.ORCID 0000-0002-9665-453X
Robert LeungProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0009-0000-4971-0941
Huitong DingDepartment of Anatomy and Neurobiology, Chobanian & Avedisian School of Medicine, Boston University, Boston, MA, United States.ORCID 0000-0001-6495-3087
Stephen B ErbanDivision of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0000-0003-1011-2324
Bruce R WeinsteinDivision of General Internal Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0009-0002-7491-3331
Yurima Guilarte-WalkerDivision of Health Informatics and Implementation Science, Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0000-0003-3610-0452
Adrian H ZaiDivision of Health Informatics and Implementation Science, Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0000-0002-2972-6839
Allan J WalkeyDivision of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0000-0003-4685-6894
Apurv SoniProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0000-0001-5049-3657
David D McManusProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0000-0002-9343-6203
Honghuang LinProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID 0000-0003-3043-3942

Funding

The Center for Advancing Point of Care in Heart, Lung, Blood and Sleep DiseasesU54HL143541 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI BRYAN O BUCHHOLZ, Nathaniel Scott Hafer · 2018 to 2026
$141.0M
RURAL: Risk Underlying Rural Areas Longitudinal Cohort StudyU01HL146382 · NHLBI · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI JUDD, SUZANNE E, RAMACHANDRAN, VASAN · 2019 to 2025
$42.5M
Validating novel sleep sensors and devices in older adults with Alzheimer's diseaseP30AG073107 · NIA · UNIVERSITY OF MASSACHUSETTS AMHERST · PI Benjamin M. Marlin · 2021 to 2026
$32.0M
Social and behavioral determinants of health and Alzheimer’s Disease: Cohort study of the US military veteran populationR01AG080670 · NIA · UNIVERSITY OF MASSACHUSETTS LOWELL · PI HONG YU · 2023 to 2026
$4.5M
A phase I trial of AdKCNH2-G628S gene therapy for post-op atrial fibrillationR33HL158541 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI DONAHUE, J KEVIN, MCMANUS, DAVID D. · 2022 to 2023
$3.4M
SUPPORT-AF IVR01HL155343 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI KAPOOR, ALOK · 2021 to 2025
$3.4M
Assessing Alzheimer disease risk and heterogeneity using multimodal machine learning approachesU01AG068221 · NIA · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI DESTEFANO, ANITA L, LIN, HONGHUANG · 2021 to 2024
$2.5M
Digital Cognitive Assessment of Preclinical Alzheimer's Disease and Related DementiasR01AG083735 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Rhoda Au, Vijaya B. Kolachalama · 2024 to 2026
$2.4M
Sleep pathology and cardiac agingR21HL175584 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LIN, HONGHUANG, THOMAS, ROBERT JOSEPH · 2024 to 2025
$233k
NHLBI NIH HHS R01 HL155343NHLBI NIH HHS R21 HL175584NHLBI NIH HHS R33 HL158541NHLBI NIH HHS U01 HL146382NHLBI NIH HHS U54 HL143541NIA NIH HHS P30 AG073107NIA NIH HHS R01 AG080670NIA NIH HHS R01 AG083735NIA NIH HHS U01 AG068221
6 · The paper itself

Abstract

backgroundScreening for cognitive impairment in primary care is important, yet primary care physicians (PCPs) report conducting routine cognitive assessments for less than half of patients older than 60 years of age. Linus Health's Core Cognitive Evaluation (CCE), a tablet-based digital cognitive assessment, has been used for the detection of cognitive impairment, but its application in primary care is not yet studied.

objectiveThis study aimed to explore the integration of CCE implementation in a primary care setting.

methodsA cohort of participants was recruited from the upcoming schedules of participating PCPs at UMass Memorial Medical Center. Eligibility criteria included individuals aged ≥65 years; ability to read, write, and speak in English or Spanish; no previous diagnosis of cognitive impairment; and no known untreated hearing or vision impairment. Research coordinators collected consent from participants and facilitated the screening process. PCPs reviewed reports in real time, immediately before the scheduled visits, and shared results at their discretion. A report was uploaded to each participant's REDCap (Research Electronic Data Capture; Vanderbilt University) record and linked to the encounter in the electronic health record. Feedback from patients and their caregivers (if applicable) was collected by a tablet-based survey in the clinic before and after screening. Participating PCPs were interviewed following the completion of the study.

resultsThe screened cohort included 150 patients with a mean age of 74 (SD 7) years, of whom 65% (97/150) were female. The CCE identified 40 patients as borderline and 7 as positive for cognitive impairment. A total of 84 orders were placed for select laboratory tests or referrals to neurology and neuropsychology within 20 days of CCE administration. Before the assessment, 95% (143/150) of patients and all 15 caregivers expressed a desire to know if their or their loved one's brain health was declining. All except one patient also completed the postassessment survey. Among them, 96% (143/149) of patients reported finding the CCE easy to complete, and 70% (105/149) felt that the experience was beneficial. In addition, 87% (130/149) of patients agreed or strongly agreed that they wanted to know their CCE results. Among the 7 participating PCPs, 6 stated that the CCE results influenced their patient care management, and all 7 indicated they would continue using the CCE if it were made available after the study.

conclusionsWe explored the integration of the CCE into primary care visits, which showed minimal disruption to the practice workflow. Future studies will be warranted to further validate the implementation of digital cognitive impairment screening tools within primary care settings in the real world.

Indexed as

Cognitive DysfunctionMass ScreeningOutpatientsAgedAged, 80 and overAmbulatory CareCohort StudiesComputers, HandheldFemaleHumansMalePrimary Health CareassessmentCCEcognitioncognitivecognitive assessmentcognitive evaluationcognitive healthcognitive impairmentcohortCore Cognitive Evaluationdigitaldigital assessmentimpairmentoutpatientprimary care

Identifiers

PMID40073397
PMCPMC11947626

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Registered trials

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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 OpenQuestion graph.