Search

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.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.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.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.AI-assisted telehealth interventions were efficient and promising and could be an effective care delivery method in nursing. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: Clinical practice guidelines (CPGs) are common tools for clinicians in daily practice. In order to use CPGs effectively at the point of care, representing CPGs into computer-interpretable format is essential. Since computer-interpretable guidelines (CIGs) have been reported to increase clinicians’ usage of guidelines and improve patient''s outcomes, it is critical to assess health care knowledge translated from CPGs into CIGs. The overall goal of this study was to illustrate the steps involved in encoding a guideline in guideline interchange format-3 (GLIF3) through a case study of a depression screening and management CPG for a nursing decision support system (DSS). Methods: This study consisted of five steps: (1) Selection of a CPG; (2) extraction and categorization in GLIF3 of concepts related to depression screening and management tasks; (3) converting GLIF3 steps to a flowchart; (4) creation of scenarios representing possible execution paths; (5) guideline execution engine (GLEE) execution of scenarios. Discussion: This study contributes to the body of knowledge regarding creation of CIGs and the use of GLEE as an evaluation tool for the encoded CIG in GLIF format for a depression CPG. CPG representation using GLIF3 and its evaluation by GLEE are useful methods to prepare nursing CPGs for implementation in a DSS. [Copyright &y& Elsevier]