Article type
Abstract
Background: The effective implementation of Clinical Practice Guidelines (CPGs) is a multifaceted challenge. Recent advancements in computer technology, particularly artificial intelligence (AI), have been pivotal in enhancing CPG adoption.
Objective: This scoping review aims to identify and synthesize the key applications of computer technology and AI in facilitating the implementation of CPGs.
Methods: A comprehensive literature search was conducted across PubMed, Embase, Web of Science, the Cochrane Library, China National Knowledge Infrastructure database, WanFang DATA, VIP database, and China Biology Medicine disc database up to December 2021. Studies employing computer technology and AI to advance CPG implementation were included.
Results: A total of 10429 published articles were identified, 117 met the inclusion criteria. 21 (17.9%) focused on the utilization of AI techniques to classify or extract the relative content of CPGs, such as recommendation sentence, condition-action sentences. 47 (40.2%) focused on the utilization of computer technology to represent guideline knowledge to make it understandable by computer. 15 (12.8%) focused on the utilization of AI techniques to verify the relative content of CPGs, such as conciliation of multiple single-disease guidelines for comorbid patients. 34 (29.1%) focused on the utilization of AI techniques to integrate guideline knowledge into different resources, such as clinical decision support systems.
Conclusions: The application of computer technology and AI in CPG implementation predominantly concentrated on the classification and extraction of guideline content, representation, verification and integration of guideline knowledge. Pattern-based algorithms and machine learning were prevalent in content classification and extraction, while knowledge representation techniques were widely used across all other domains.
Objective: This scoping review aims to identify and synthesize the key applications of computer technology and AI in facilitating the implementation of CPGs.
Methods: A comprehensive literature search was conducted across PubMed, Embase, Web of Science, the Cochrane Library, China National Knowledge Infrastructure database, WanFang DATA, VIP database, and China Biology Medicine disc database up to December 2021. Studies employing computer technology and AI to advance CPG implementation were included.
Results: A total of 10429 published articles were identified, 117 met the inclusion criteria. 21 (17.9%) focused on the utilization of AI techniques to classify or extract the relative content of CPGs, such as recommendation sentence, condition-action sentences. 47 (40.2%) focused on the utilization of computer technology to represent guideline knowledge to make it understandable by computer. 15 (12.8%) focused on the utilization of AI techniques to verify the relative content of CPGs, such as conciliation of multiple single-disease guidelines for comorbid patients. 34 (29.1%) focused on the utilization of AI techniques to integrate guideline knowledge into different resources, such as clinical decision support systems.
Conclusions: The application of computer technology and AI in CPG implementation predominantly concentrated on the classification and extraction of guideline content, representation, verification and integration of guideline knowledge. Pattern-based algorithms and machine learning were prevalent in content classification and extraction, while knowledge representation techniques were widely used across all other domains.