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Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

BACKGROUND: Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. OBJECTIVE: The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and...

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Detalles Bibliográficos
Autores principales: Choudhury, Avishek, Asan, Onur
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7414411/
https://www.ncbi.nlm.nih.gov/pubmed/32706688
http://dx.doi.org/10.2196/18599
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author Choudhury, Avishek
Asan, Onur
author_facet Choudhury, Avishek
Asan, Onur
author_sort Choudhury, Avishek
collection PubMed
description BACKGROUND: Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. OBJECTIVE: The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. METHODS: We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. RESULTS: We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. CONCLUSIONS: This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.
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spelling pubmed-74144112020-08-20 Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review Choudhury, Avishek Asan, Onur JMIR Med Inform Review BACKGROUND: Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. OBJECTIVE: The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. METHODS: We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. RESULTS: We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. CONCLUSIONS: This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings. JMIR Publications 2020-07-24 /pmc/articles/PMC7414411/ /pubmed/32706688 http://dx.doi.org/10.2196/18599 Text en ©Avishek Choudhury, Onur Asan. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 24.07.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Review
Choudhury, Avishek
Asan, Onur
Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review
title Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review
title_full Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review
title_fullStr Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review
title_full_unstemmed Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review
title_short Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review
title_sort role of artificial intelligence in patient safety outcomes: systematic literature review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7414411/
https://www.ncbi.nlm.nih.gov/pubmed/32706688
http://dx.doi.org/10.2196/18599
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