Artificial intelligence assisted telehealth for nursing: A scoping review

Abstract

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.

Artificial intelligence–enabled care equipment

“…Their versatility allows for seamless integration into various health care settings, offering significant potential to enhance patient monitoring, diagnostics, and overall health management 50 . There are various types of smart wearable devices, which can be categorized into head wearable devices [51][52][53][54] , torso wearable devices 51,[53][54][55][56] , limb wearable devices 51,54,[56][57][58][59][60] , and other types of wearable devices 51,54,57,61 according to their different wearing parts; according to the type of integration technology classification, they can be divided into inertial wearable devices [53][54][55] , optical wearable devices 52,53,58 , temperature wearable devices 51 , stress wearable devices 54,57,59 , electrochemical wearable devices [56][57][58]60 , biowearable devices 51,60 , and so on 29,62 . Wearable devices dynamically detect biochemical indicators such as blood glucose 51,61 , blood pressure 52,57 , heart rate…”

“…In addition, it enables optimal decisionmaking for seniors or patients in situations where professional caregivers are lacking or insufficient 27 . A growing body of research confirms that AI care equipment plays a major role in reducing health care costs, addressing the uneven distribution of regional health care resources, improving medical efficiency, and responding to public health emergencies 28,29 . In 2021, the World Health Organization (WHO) released a global strategy on digital health 2020-2025, which argued that AI care equipment, such as smart wearable devices, can provide patients with a full range of full-time care and have the potential to improve existing health care processes and provide patients with person-centered care 30 .…”

Perspectives on Artificial Intelligence in Nursing in Asia

“…Nurses are at the forefront of this revolution. AI can augment nurses’ abilities, thus improving patient outcomes and increasing clinicians’ and patients’ satisfaction [ 7 - 10 ].…”