Evidence map›Paper›PMID 40515731›Full record

ArticleJournal of the American Geriatrics Society2025

NOTICE-ED: Nurse or Technician Insights Into Cognitive Evaluations in the Emergency Department.

Sarah J Nessen, Anita N Chary, Annika R Bhananker, K Jane Muir, Lauren T Southerland, Kyra O'Brien, Ari B Friedman

Abstract read
In one paragraph

Article in Journal of the American Geriatrics Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Sarah J NessenPerelman School of Medicine, Penn Center for Emergency Care and Policy Research, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0009-0002-4049-1759
Anita N CharyDepartment of Emergency Medicine, Baylor College of Medicine, Houston, Texas, USA.
Annika R BhanankerCenter for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, Texas, USA.
K Jane MuirCenter for Health Outcomes and Policy Research, University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania, USA.
Lauren T SoutherlandDepartment of Emergency Medicine, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA.ORCID 0000-0002-3561-8332
Kyra O'BrienDepartment of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Ari B FriedmanLeonard Davis Institute of Health Economics, Philadelphia, Pennsylvania, USA.

Funding

Abdominal Pain in Older Patients in Emergency DepartmentsK23AG080061 · NIA · UNIVERSITY OF PENNSYLVANIA · PI Ari B Friedman · 2023 to 2026
$780k
Identifying Nursing Models of Care to Improve Emergency Department Patient OutcomesK01NR021419 · NINR · UNIVERSITY OF PENNSYLVANIA · PI Kathryn Jane Muir · 2025 to 2026
$337k
Trajectories of Frailty and Cognitive Impairment in Older AdultsR03AG078933 · NIA · UNIVERSITY OF PENNSYLVANIA · PI FRIEDMAN, ARI B · 2022 to 2023
$325k
Identifying Implementation Strategies for Emergency Department (ED) Delirium Screening in Older AdultsR03AG078943 · NIA · BAYLOR COLLEGE OF MEDICINE · PI CHARY, ANITA · 2022 to 2023
$320k
Houston Veterans Administration Health Services Research and Development Center for Innovations in Quality, Effectiveness, and Safety CIN13-413NIA NIH HHS K23 AG080061NIA NIH HHS R03 AG078933NIA NIH HHS R03AG078933NIA NIH HHS R03 AG078943NIA NIH HHS R03AG078943NINR NIH HHS K01 NR021419
6 · The paper itself

Abstract

backgroundSeveral strategies have been proposed to increase chronic cognitive impairment (CI) screening in the emergency department (ED). Our goal was to assess the feasibility and acceptability of implementing specific CI screening tools and strategies in the ED from an ED registered nurse and technician perspective.

methodsWe performed a qualitative study using semi-structured interviews with a purposive sample of ED nurses and ED technicians (EDTs). Participants worked at an urban academic hospital and were interviewed between November 2023 and March 2024. Interviews assessed participants' opinions on the feasibility and acceptability of CI screening and the use of machine learning (ML) tools to identify high-risk patients for targeted CI screening, tablet-based screenings, and two validated CI screenings: the Ottawa 3DY (O3DY) and Short Blessed Test (SBT). We used the Consolidated Framework for Implementation Research (CFIR) to develop our interview guide and performed a rapid analysis with deductive and inductive codes based on CFIR constructs.

resultsFour major themes related to CI screening tools arose: (1) Benefits of CI screening; (2) feasibility of integrating screening tools into existing workflows; (3) professional role limitations; and (4) implementation requirements. Participants perceived CI screening as important for allocating limited ED resources. Shorter, less specific testing, including the O3DY, was seen as feasible during triage, while longer, more specific screening, including the SBT, was seen as more feasible in roomed care areas. Both ED nurses and EDTs identified the need for electronic health record tools and dedicated screening teams to facilitate implementation.

conclusionED nurses and EDTs support chronic CI screening if screening techniques and clinical teams can be optimized to make workflows feasible.

Indexed as

Cognitive DysfunctionEmergency Service, HospitalMass ScreeningAdultAgedAttitude of Health PersonnelFeasibility StudiesFemaleHumansInterviews as TopicMachine LearningMaleMiddle AgedQualitative Researchcognitive impairmentemergency medicinemachine learningqualitative researchscreening

Identifiers

PMID40515731
PMCPMC12396171

What OpenQuestion holds

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Registered trials

None linked

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.