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人们如何看待语音和视频识别技术在急诊医疗实践中的应用?

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

机构信息

Department of Emergency Medicine, Seoul National University Hospital, Seoul, Republic of Korea.

Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.

出版信息

PLoS One. 2022 Sep 23;17(9):e0275280. doi: 10.1371/journal.pone.0275280. eCollection 2022.


DOI:10.1371/journal.pone.0275280
PMID:36149899
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9506645/
Abstract

BACKGROUND: Recently, 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. METHODS: Structured 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. RESULTS: Of 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)). CONCLUSION: A 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.

摘要

背景: 最近,语音和视频信息识别技术(SVRT)发展迅速。将 SVRT 引入急诊医疗实践过程中可能会改善医疗保健。本研究旨在评估患者、护理人员和急诊医务人员对 SVRT 的接受程度。

方法: 为患者或护理人员组和急诊医务人员组制定了结构化问卷。调查在一家三级学术医院急诊部进行。为每个特定群体优化了问题,并主要使用李克特 5 级量表提供了回答。还对整个队列和亚组进行了多变量逻辑回归分析,以计算优势比(OR)和置信区间(CI),以检查个体特征与 SVRT 接受度之间的关联。

结果: 在 264 名参与者中,受访者对 SVRT 和人工智能(AI)在未来的应用表现出积极的态度和接受度;179 人(67.8%)对视频记录,190 人(72.0%)对语音记录。多变量逻辑回归模型显示,在急诊医疗实践中,SVRT 接受度与几个因素相关:信号分析技术(OR,95%CI:2.48(1.15-5.42))和 AI(OR,95%CI:1.70(0.91-3.17))对医疗保健改善的信念、AI 在急诊医学中的可靠性(OR,95%CI:2.36(1.28-4.35))和个人信息的安全性(OR,95%CI:1.98(1.10-3.63))。

结论: 患者或护理人员对 SVRT 表现出高度的接受度,这似乎与对新技术、AI 和个人信息安全的积极态度相关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d05/9506645/ac8aeec4a850/pone.0275280.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d05/9506645/dde12f539818/pone.0275280.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d05/9506645/ac8aeec4a850/pone.0275280.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d05/9506645/dde12f539818/pone.0275280.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d05/9506645/ac8aeec4a850/pone.0275280.g002.jpg

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本文引用的文献

[1]
Do you mind if I record?: Perceptions and practice regarding patient requests to record clinic visits in oncology.

Cancer. 2022-1-15

[2]
'Mind if I record this?' Patients making audio-visual recordings of consultations: a survey of surgeons' experiences.

Ann R Coll Surg Engl. 2022-1

[3]
Speech Technology for Healthcare: Opportunities, Challenges, and State of the Art.

IEEE Rev Biomed Eng. 2021

[4]
Acceptance of mHealth Apps for Self-Management Among People with Hypertension.

Stud Health Technol Inform. 2019-9-3

[5]
Evaluation of user satisfaction and usability of a mobile app for smoking cessation.

Comput Methods Programs Biomed. 2019-8-23

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Yearb Med Inform. 2019-8

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Emergency department monitor alarms rarely change clinical management: An observational study.

Am J Emerg Med. 2019-7-30

[8]
Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images.

J Ophthalmol. 2019-4-9

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Web System Prototype based on speech recognition to construct medical reports in Brazilian Portuguese.

Int J Med Inform. 2018-10-26

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