Evidence map›Paper›PMID 36749563›Full record

ArticleJournal of cardiovascular translational research2023

Attitudes About Artificially Intelligent Interactive Voice Response Systems Using Amazon Alexa in Cardiovascular Clinics: Insights from the VOICE-COVID-19 Study.

Elie Ganni, Amir Razaghizad, Emily Oulousian, Peter Chung, Jiayi Ni, Robert Avram, Abhinav Sharma

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cardiovascular translational research, 2023. 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. Review
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.

Elie GanniDREAM-CV Lab, McGill University Health Centre, McGill University, Montreal, QC, Canada.
Amir RazaghizadDREAM-CV Lab, McGill University Health Centre, McGill University, Montreal, QC, Canada.
Emily OulousianDREAM-CV Lab, McGill University Health Centre, McGill University, Montreal, QC, Canada.
Peter ChungDREAM-CV Lab, McGill University Health Centre, McGill University, Montreal, QC, Canada.
Jiayi NiDREAM-CV Lab, McGill University Health Centre, McGill University, Montreal, QC, Canada.
Robert AvramMontreal Heart Institute, University of Montreal, Montreal, QC, Canada.
Abhinav SharmaDREAM-CV Lab, McGill University Health Centre, McGill University, Montreal, QC, Canada. Abhinav.Sharma@mail.mcgill.ca.ORCID 0000-0002-2346-8330

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The acceptability of artificially intelligent interactive voice response (AI-IVR) systems in cardiovascular research settings is unclear. As a result, we evaluated peoples' attitudes regarding the Amazon Echo Show 8 device when used for electronic data capture in cardiovascular clinics. Participants were recruited following the Voice-Based Screening for SARS-CoV-2 Exposure in Cardiovascular clinics study. Overall, 215 people enrolled and underwent screening (mean age 46.1; 55% females) in the VOICE-COVID study and 58 people consented to participate in a post-screening survey. Following thematic analysis, four key themes affecting AI-IVR acceptability were identified. These were difficulties with communication (44.8%), limitations with available interaction modalities (41.4%), barriers with the development of therapeutic relationships (25.9%), and concerns with universality and accessibility (8.6%). While there are potential concerns with the use of AI-IVR technologies, these systems appeared to be well accepted in cardiovascular clinics. Increased development of these technologies could significantly improve healthcare access and efficiency.

Indexed as

COVID-19AttitudeFemaleHumansMaleMiddle AgedSARS-CoV-2AlexaAmazonCOVID-19SARS-CoV2Voice-based technologies

Identifiers

PMID36749563
PMCPMC9904249

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.