Introduction: In primary care, almost 75% of outpatient visits by family doctors and general practitioners involve continuation or initiation of drug therapy. Due to the enormous amount of drugs used by outpatients in unmonitored situations, the potential risk of adverse events due to an error in the use or prescription of drugs is much higher than in a hospital setting. Artificial intelligence (AI) application can help healthcare professionals to take charge of patient safety by improving error detection, patient stratification and drug management. The aim is to investigate the impact of AI algorithms on drug management in primary care settings and to compare AI or algorithms with standard clinical practice to define the medication fields where a technological support could lead to better results. Methods and analysis: A systematic review and meta-analysis of literature will be conducted querying PubMed, Cochrane and ISI Web of Science from the inception to December 2021. The primary outcome will be the reduction of medication errors obtained by AI application. The search strategy and the study selection will be conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the population, intervention, comparator and outcome framework. Quality of included studies will be appraised adopting the quality assessment tool for observational cohort and cross-sectional studies for non-randomised controlled trials as well as the quality assessment of controlled intervention studies of National Institute of Health for randomised controlled trials. Ethics and dissemination: Formal ethical approval is not required since no human beings are involved. The results will be disseminated widely through peer-reviewed publications.

Assessing the potentiality of algorithms and artificial intelligence adoption to disrupt patient primary care with a safer and faster medication management: a systematic review protocol / Oliva, Antonio; Altamura, Gerardo; Nurchis, Mario Cesare; Zedda, Massimo; Sessa, Giorgio; Cazzato, Francesca; Aulino, Giovanni; Sapienza, Martina; Riccardi, Maria Teresa; Della Morte, Gabriele; Caputo, Matteo; Grassi, Simone; Damiani, Gianfranco. - In: BMJ OPEN. - ISSN 2044-6055. - ELETTRONICO. - 12:(2022), pp. 1-4. [10.1136/bmjopen-2021-057399]

Assessing the potentiality of algorithms and artificial intelligence adoption to disrupt patient primary care with a safer and faster medication management: a systematic review protocol

Grassi, Simone;
2022

Abstract

Introduction: In primary care, almost 75% of outpatient visits by family doctors and general practitioners involve continuation or initiation of drug therapy. Due to the enormous amount of drugs used by outpatients in unmonitored situations, the potential risk of adverse events due to an error in the use or prescription of drugs is much higher than in a hospital setting. Artificial intelligence (AI) application can help healthcare professionals to take charge of patient safety by improving error detection, patient stratification and drug management. The aim is to investigate the impact of AI algorithms on drug management in primary care settings and to compare AI or algorithms with standard clinical practice to define the medication fields where a technological support could lead to better results. Methods and analysis: A systematic review and meta-analysis of literature will be conducted querying PubMed, Cochrane and ISI Web of Science from the inception to December 2021. The primary outcome will be the reduction of medication errors obtained by AI application. The search strategy and the study selection will be conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the population, intervention, comparator and outcome framework. Quality of included studies will be appraised adopting the quality assessment tool for observational cohort and cross-sectional studies for non-randomised controlled trials as well as the quality assessment of controlled intervention studies of National Institute of Health for randomised controlled trials. Ethics and dissemination: Formal ethical approval is not required since no human beings are involved. The results will be disseminated widely through peer-reviewed publications.
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Oliva, Antonio; Altamura, Gerardo; Nurchis, Mario Cesare; Zedda, Massimo; Sessa, Giorgio; Cazzato, Francesca; Aulino, Giovanni; Sapienza, Martina; Riccardi, Maria Teresa; Della Morte, Gabriele; Caputo, Matteo; Grassi, Simone; Damiani, Gianfranco
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/1269237
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