Evidence map›Paper›PMID 36149899›Full record

ArticlePloS one2022

How do people think about the implementation of speech and video recognition technology in emergency medical practice?

Ki Hong Kim, Ki Jeong Hong, Sang Do Shin, Young Sun Ro, Kyoung Jun Song, Tae Han Kim, Jeong Ho Park, Joo Jeong

Abstract read
In one paragraph

Article in PloS one, 2022. 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

8 authors.

Ki Hong KimDepartment of Emergency Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Ki Jeong HongDepartment of Emergency Medicine, Seoul National University Hospital, Seoul, Republic of Korea.ORCID 0000-0003-3334-817X
Sang Do ShinDepartment of Emergency Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Young Sun RoDepartment of Emergency Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Kyoung Jun SongDepartment of Emergency Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.ORCID 0000-0002-0027-6352
Tae Han KimDepartment of Emergency Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.ORCID 0000-0003-3855-081X
Jeong Ho ParkDepartment of Emergency Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Joo JeongDepartment of Emergency Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRecently, speech and video information recognition technology (SVRT) has developed rapidly. Introducing SVRT into the emergency medical practice process may lead to improvements in health care. The purpose of this study was to evaluate the level of acceptance of SVRT among patients, caregivers and emergency medical staff.

methodsStructured questionnaires were developed for the patient or caregiver group and the emergency medical staff group. The survey was performed in one tertiary academic hospital emergency department. Questions were optimized for each specific group, and responses were provided mostly using Likert 5-scales. Additional multivariable logistic regression analyses for the whole cohort and subgroups were conducted to calculate odds ratios (OR) and confidence intervals (CI) to examine the association between individual characteristics and SVRT acceptance.

resultsOf 264 participants, respondents demonstrated a positive attitude and acceptance toward SVRT and artificial intelligence (AI) in future; 179 (67.8%) for video recordings, and 190 (72.0%) for speech recordings. A multivariable logistic regression model revealed that several factors were associated with acceptance of SVRT in emergency medical practice: belief in health care improvement by signal analysis technology (OR, 95% CIs: 2.48 (1.15-5.42)) and AI (OR, 95% CIs: 1.70 (0.91-3.17)), reliability of AI application in emergency medicine (OR, 95% CIs: 2.36 (1.28-4.35)) and the security of personal information (OR, 95% CIs: 1.98 (1.10-3.63)).

conclusionA high level of acceptance toward SVRT has been shown in patients or caregivers, and it also appears to be associated with positive attitudes toward new technology, AI and security of personal information.

Indexed as

Artificial IntelligenceEmergency MedicineHumansReproducibility of ResultsSpeechSurveys and QuestionnairesTechnology

Identifiers

PMID36149899
PMCPMC9506645

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.