Evidence map›Paper›PMID 36787882›Full record

ArticleApplied clinical informatics2023

Implementation Fidelity of Chatbot Screening for Social Needs: Acceptability, Feasibility, Appropriateness.

Raina Langevin, Andrew B L Berry, Jinyang Zhang, Callan E Fockele, Layla Anderson, Dennis Hsieh, Andrea Hartzler, Herbert C Duber, Gary Hsieh

Abstract read
In one paragraph

Article in Applied clinical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Supporting Emergency Department Patients Experiencing Homelessness.Journal of the American College of Emergency Physicians open · 2026
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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

9 authors.

Raina LangevinDepartment of Human Centered Design and Engineering, University of Washington, Seattle, Washington, United States.
Andrew B L BerryDepartment of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States.
Jinyang ZhangPaul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, United States.
Callan E FockeleDepartment of Emergency Medicine, University of Washington School of Medicine, Seattle, Washington, United States.
Layla AndersonDepartment of Emergency Medicine, University of Washington School of Medicine, Seattle, Washington, United States.
Dennis HsiehDepartment of Emergency Medicine, Harbor-University of California Los Angeles Medical Center, Torrance, California, United States.
Andrea HartzlerBiomedical Informatics and Medical Education, University of Washington, Seattle, Washington, United States.
Herbert C DuberDepartment of Emergency Medicine, University of Washington School of Medicine, Seattle, Washington, United States.
Gary HsiehDepartment of Human Centered Design and Engineering, University of Washington, Seattle, Washington, United States.

Funding

Transform Dissemination and Implementation Science in CTSA ProgramsUL1TR002319 · NCATS · UNIVERSITY OF WASHINGTON · PI John K. Amory · 2017 to 2026
$100.0M
NCATS NIH HHS UL1 TR002319
6 · The paper itself

Abstract

objectivesPatient and provider-facing screening tools for social determinants of health have been explored in a variety of contexts; however, effective screening and resource referral remain challenging, and less is known about how patients perceive chatbots as potential social needs screening tools. We investigated patient perceptions of a chatbot for social needs screening using three implementation outcome measures: acceptability, feasibility, and appropriateness.

methodsWe implemented a chatbot for social needs screening at one large public hospital emergency department (ED) and used concurrent triangulation to assess perceptions of the chatbot use for screening. A total of 350 ED visitors completed the social needs screening and rated the chatbot on implementation outcome measures, and 22 participants engaged in follow-up phone interviews.

resultsThe screened participants ranged in age from 18 to 90 years old and were diverse in race/ethnicity, education, and insurance status. Participants (

conclusionIn this study, we deployed a chatbot for social needs screening in a real-world context and found patients perceived the chatbot to be an acceptable, feasible, and appropriate modality for social needs screening. Findings suggest that chatbots are a promising modality for social needs screening and can successfully engage a large, diverse patient population in the ED. This is significant, as it suggests that chatbots could facilitate a screening process that ultimately connects patients to care for social needs, improving health and well-being for members of vulnerable patient populations.

Indexed as

Emergency Service, HospitalReferral and ConsultationAdolescentAdultAgedAged, 80 and overFeasibility StudiesHumansMiddle AgedResearch DesignSoftwareYoung Adult

Identifiers

PMID36787882
PMCPMC10191737

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.