Evidence map›Paper›PMID 41700962›Full record

ArticleAJOB empirical bioethics

Primary Care Physician Perspectives on Artificial Intelligence for Dementia Screening: A Qualitative Study.

Hannah G Hanson, Jalayne J Arias, Heather M Wurtz, Margaret Manchester, Catherine Scipion, Alex D Federman

Abstract read
In one paragraph

Article in AJOB empirical bioethics. 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.

Hannah G HansonDivision of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Jalayne J AriasDepartment of Health Policy and Behavioral Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, USA.
Heather M WurtzDepartment of Anthropology, College of Liberal Arts and Sciences, University of Connecticut, Storrs, Connecticut, USA.
Margaret ManchesterDepartment of Health Policy and Behavioral Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, USA.
Catherine ScipionDepartment of Health Policy and Behavioral Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, USA.
Alex D FedermanDivision of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

Funding

Natural Language Processing and Automated Speech Recognition to Identify Older Adults with Cognitive Impairment SupplementR01AG066471 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI FEDERMAN, ALEX D, WISNIVESKY, JUAN P · 2020 to 2024
$4.5M
Home-based Primary Care for Homebound Seniors: a Randomized Controlled TrialR01AG052557 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI FEDERMAN, ALEX D · 2016 to 2020
$3.5M
NIA NIH HHS R01 AG052557NIA NIH HHS R01 AG066471
6 · The paper itself

Abstract

backgroundAutomated approaches to cognitive impairment screening may soon achieve sufficient levels of accuracy for clinical implementation but they present potentially serious ethical challenges. Overcoming such challenges for successful implementation of automated screening may depend on the perspective of the clinicians who are its intended users.

methodsWe conducted a qualitative study of internal medicine, geriatrics and family medicine physicians recruited from ambulatory practices in New York City, NY to identify ethical challenges of implementing automated CI screening (

resultsParticipants reported routine screening for cognitive impairment only in the context of the Medicare annual wellness visit and generally avoided routine screening because of limited treatment options to support patients, such as poor access to geriatrics/neuropsychiatric services. Thematic analysis revealed several perceived benefits: enhanced clinical efficiency, engagement in cognitive healthcare of their patients, and expanded access to cognitive care. They identified several potential challenges with ethics implications: concerns about accuracy of the technology, bias, difficulty communicating the technology and results to patients and caregivers that could impact their ability to provide informed consent, and risks to patients (privacy, stigmatization and insurability). They noted that without more health system infrastructure to support patients with dementia, the benefits of automated screening would be limited.

conclusionsPhysicians identified several critical ethical challenges to automated CI screening. Health systems will need to address these challenges to ensure benefit for and the safety, autonomy and privacy of patients and ultimately, the successful implementation of such technology.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelCognitive DysfunctionDementiaMass ScreeningPhysicians, Primary CareAdultAgedDigital HealthElectronic Health RecordsFemaleFocus GroupsHumansMaleMiddle AgedNew York CityAlzheimer’s diseaseArtificial intelligencecognitive impairmentdementiaphysician perspectivesscreening

Identifiers

PMID41700962
PMCPMC13678654

What OpenQuestion holds

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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.