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Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review
OBJECTIVES: The aim of this study is to investigate the effect of artificial intelligence (AI) and/or algorithms on drug management in primary care settings comparing AI and/or algorithms with standard clinical practice. Second, we evaluated what is the most frequently reported type of medication er...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BMJ Publishing Group
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10040015/ https://www.ncbi.nlm.nih.gov/pubmed/36958780 http://dx.doi.org/10.1136/bmjopen-2022-065301 |
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author | Damiani, Gianfranco Altamura, Gerardo Zedda, Massimo Nurchis, Mario Cesare Aulino, Giovanni Heidar Alizadeh, Aurora Cazzato, Francesca Della Morte, Gabriele Caputo, Matteo Grassi, Simone Oliva, Antonio |
author_facet | Damiani, Gianfranco Altamura, Gerardo Zedda, Massimo Nurchis, Mario Cesare Aulino, Giovanni Heidar Alizadeh, Aurora Cazzato, Francesca Della Morte, Gabriele Caputo, Matteo Grassi, Simone Oliva, Antonio |
author_sort | Damiani, Gianfranco |
collection | PubMed |
description | OBJECTIVES: The aim of this study is to investigate the effect of artificial intelligence (AI) and/or algorithms on drug management in primary care settings comparing AI and/or algorithms with standard clinical practice. Second, we evaluated what is the most frequently reported type of medication error and the most used AI machine type. METHODS: A systematic review of literature was conducted querying PubMed, Cochrane and ISI Web of Science until November 2021. The search strategy and the study selection were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the Population, Intervention, Comparator, Outcome framework. Specifically, the Population chosen was general population of all ages (ie, including paediatric patients) in primary care settings (ie, home setting, ambulatory and nursery homes); the Intervention considered was the analysis AI and/or algorithms (ie, intelligent programs or software) application in primary care for reducing medications errors, the Comparator was the general practice and, lastly, the Outcome was the reduction of preventable medication errors (eg, overprescribing, inappropriate medication, drug interaction, risk of injury, dosing errors or in an increase in adherence to therapy). The methodological quality of included studies was appraised adopting the Quality Assessment of Controlled Intervention Studies of the National Institute of Health for randomised controlled trials. RESULTS: Studies reported in different ways the effective reduction of medication error. Ten out of 14 included studies, corresponding to 71% of articles, reported a reduction of medication errors, supporting the hypothesis that AI is an important tool for patient safety. CONCLUSION: This study highlights how a proper application of AI in primary care is possible, since it provides an important tool to support the physician with drug management in non-hospital environments. |
format | Online Article Text |
id | pubmed-10040015 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-100400152023-03-27 Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review Damiani, Gianfranco Altamura, Gerardo Zedda, Massimo Nurchis, Mario Cesare Aulino, Giovanni Heidar Alizadeh, Aurora Cazzato, Francesca Della Morte, Gabriele Caputo, Matteo Grassi, Simone Oliva, Antonio BMJ Open Public Health OBJECTIVES: The aim of this study is to investigate the effect of artificial intelligence (AI) and/or algorithms on drug management in primary care settings comparing AI and/or algorithms with standard clinical practice. Second, we evaluated what is the most frequently reported type of medication error and the most used AI machine type. METHODS: A systematic review of literature was conducted querying PubMed, Cochrane and ISI Web of Science until November 2021. The search strategy and the study selection were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the Population, Intervention, Comparator, Outcome framework. Specifically, the Population chosen was general population of all ages (ie, including paediatric patients) in primary care settings (ie, home setting, ambulatory and nursery homes); the Intervention considered was the analysis AI and/or algorithms (ie, intelligent programs or software) application in primary care for reducing medications errors, the Comparator was the general practice and, lastly, the Outcome was the reduction of preventable medication errors (eg, overprescribing, inappropriate medication, drug interaction, risk of injury, dosing errors or in an increase in adherence to therapy). The methodological quality of included studies was appraised adopting the Quality Assessment of Controlled Intervention Studies of the National Institute of Health for randomised controlled trials. RESULTS: Studies reported in different ways the effective reduction of medication error. Ten out of 14 included studies, corresponding to 71% of articles, reported a reduction of medication errors, supporting the hypothesis that AI is an important tool for patient safety. CONCLUSION: This study highlights how a proper application of AI in primary care is possible, since it provides an important tool to support the physician with drug management in non-hospital environments. BMJ Publishing Group 2023-03-23 /pmc/articles/PMC10040015/ /pubmed/36958780 http://dx.doi.org/10.1136/bmjopen-2022-065301 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Public Health Damiani, Gianfranco Altamura, Gerardo Zedda, Massimo Nurchis, Mario Cesare Aulino, Giovanni Heidar Alizadeh, Aurora Cazzato, Francesca Della Morte, Gabriele Caputo, Matteo Grassi, Simone Oliva, Antonio Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review |
title | Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review |
title_full | Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review |
title_fullStr | Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review |
title_full_unstemmed | Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review |
title_short | Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review |
title_sort | potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10040015/ https://www.ncbi.nlm.nih.gov/pubmed/36958780 http://dx.doi.org/10.1136/bmjopen-2022-065301 |
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