{"title":"人工智能辅助远程医疗护理:范围审查。","authors":"Jeeyae Choi, Seoyoon Woo, Anastasiya Ferrell","doi":"10.1177/1357633X231167613","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Due to the COVID-19 pandemic, telehealth resurfaced as a convenient efficient healthcare delivery method. Researchers indicate that Artificial Intelligence (AI) could further facilitate delivering quality care in telehealth. It is essential to find supporting evidence to use AI-assisted telehealth interventions in nursing.</p><p><strong>Objectives: </strong>This scoping review focuses on finding users' satisfaction and perception of AI-assisted telehealth intervention, performances of AI algorithms, and the types of AI technology used.</p><p><strong>Methods: </strong>A structured search was performed in six databases, PubMed, CINAHL, Web of Science, OVID, PsycINFO, and ProQuest, following the guidance of the Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews. The quality of the final reviewed studies was assessed using the Medical Education Research Study Quality Instrument.</p><p><strong>Results: </strong>Eight of the 41 studies published between 2017 and 2022 were included in the final review. Six studies were conducted in the United States, one in Japan, and one in South Korea. Four studies collected data from participants (<i>n</i> = 3014). Two studies used image data (<i>n</i> = 1986), and two used sensor data from smart homes to detect patients' health events for nurses (<i>n</i> = 35). The quality of studies implied moderate to high-quality study (mean = 10.1, range = 7.7-13.7). Two studies reported high user satisfaction, three assessed user perception of AI in telehealth, and only one showed high AI acceptability. Two studies revealed the high performance of AI algorithms. Five studies used machine learning algorithms.</p><p><strong>Conclusions: </strong>AI-assisted telehealth interventions were efficient and promising and could be an effective care delivery method in nursing.</p>","PeriodicalId":50024,"journal":{"name":"Journal of Telemedicine and Telecare","volume":" ","pages":"140-149"},"PeriodicalIF":3.5000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence assisted telehealth for nursing: A scoping review.\",\"authors\":\"Jeeyae Choi, Seoyoon Woo, Anastasiya Ferrell\",\"doi\":\"10.1177/1357633X231167613\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Due to the COVID-19 pandemic, telehealth resurfaced as a convenient efficient healthcare delivery method. Researchers indicate that Artificial Intelligence (AI) could further facilitate delivering quality care in telehealth. It is essential to find supporting evidence to use AI-assisted telehealth interventions in nursing.</p><p><strong>Objectives: </strong>This scoping review focuses on finding users' satisfaction and perception of AI-assisted telehealth intervention, performances of AI algorithms, and the types of AI technology used.</p><p><strong>Methods: </strong>A structured search was performed in six databases, PubMed, CINAHL, Web of Science, OVID, PsycINFO, and ProQuest, following the guidance of the Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews. The quality of the final reviewed studies was assessed using the Medical Education Research Study Quality Instrument.</p><p><strong>Results: </strong>Eight of the 41 studies published between 2017 and 2022 were included in the final review. Six studies were conducted in the United States, one in Japan, and one in South Korea. Four studies collected data from participants (<i>n</i> = 3014). Two studies used image data (<i>n</i> = 1986), and two used sensor data from smart homes to detect patients' health events for nurses (<i>n</i> = 35). The quality of studies implied moderate to high-quality study (mean = 10.1, range = 7.7-13.7). Two studies reported high user satisfaction, three assessed user perception of AI in telehealth, and only one showed high AI acceptability. Two studies revealed the high performance of AI algorithms. Five studies used machine learning algorithms.</p><p><strong>Conclusions: </strong>AI-assisted telehealth interventions were efficient and promising and could be an effective care delivery method in nursing.</p>\",\"PeriodicalId\":50024,\"journal\":{\"name\":\"Journal of Telemedicine and Telecare\",\"volume\":\" \",\"pages\":\"140-149\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Telemedicine and Telecare\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/1357633X231167613\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/4/18 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Telemedicine and Telecare","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/1357633X231167613","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/4/18 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Artificial intelligence assisted telehealth for nursing: A scoping review.
Background: Due to the COVID-19 pandemic, telehealth resurfaced as a convenient efficient healthcare delivery method. Researchers indicate that Artificial Intelligence (AI) could further facilitate delivering quality care in telehealth. It is essential to find supporting evidence to use AI-assisted telehealth interventions in nursing.
Objectives: This scoping review focuses on finding users' satisfaction and perception of AI-assisted telehealth intervention, performances of AI algorithms, and the types of AI technology used.
Methods: A structured search was performed in six databases, PubMed, CINAHL, Web of Science, OVID, PsycINFO, and ProQuest, following the guidance of the Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews. The quality of the final reviewed studies was assessed using the Medical Education Research Study Quality Instrument.
Results: Eight of the 41 studies published between 2017 and 2022 were included in the final review. Six studies were conducted in the United States, one in Japan, and one in South Korea. Four studies collected data from participants (n = 3014). Two studies used image data (n = 1986), and two used sensor data from smart homes to detect patients' health events for nurses (n = 35). The quality of studies implied moderate to high-quality study (mean = 10.1, range = 7.7-13.7). Two studies reported high user satisfaction, three assessed user perception of AI in telehealth, and only one showed high AI acceptability. Two studies revealed the high performance of AI algorithms. Five studies used machine learning algorithms.
Conclusions: AI-assisted telehealth interventions were efficient and promising and could be an effective care delivery method in nursing.
期刊介绍:
Journal of Telemedicine and Telecare provides excellent peer reviewed coverage of developments in telemedicine and e-health and is now widely recognised as the leading journal in its field. Contributions from around the world provide a unique perspective on how different countries and health systems are using new technology in health care. Sections within the journal include technology updates, editorials, original articles, research tutorials, educational material, review articles and reports from various telemedicine organisations. A subscription to this journal will help you to stay up-to-date in this fast moving and growing area of medicine.