IntroductionIn 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 analysisA 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 disseminationFormal ethical approval is not required since no human beings are involved. The results will be disseminated widely through peer-reviewed publications.
ObjectivesThe 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.MethodsA 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.ResultsStudies 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.ConclusionThis 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.
Background and Objectives: Clozapine is the only antipsychotic approved for treatment-resistant schizophrenia. Despite its superior efficacy profile as compared with other antipsychotics, clozapine remains underutilized. Clozapine monitoring systems clearly describe the proposed management of clozapine-induced neutropenia; however, no specific mention is made of how to interpret neutrophilic leukocytosis, despite that being a relatively frequent finding. Prescribers unfamiliar with this molecule may misjudge its clinical significance, potentially leading to untimely treatment interruption. Here, we systematically review the literature on the risk of neutrophilic leukocytosis during clozapine treatment, and describe eight additional cases among our patient cohort. Materials and Methods: We performed a systematic review of the literature on PubMed and Embase using the PRISMA 2020 guidelines, and selected all original reports describing either (1) the prevalence of neutrophilic leukocytosis during clozapine treatment, or (2) the clinical significance of neutrophilic leukocytosis. We described eight additional cases of neutrophilic leukocytosis during clozapine treatment while attending an outpatient psychiatric clinic. Results: Our research ultimately yielded the selection of 13 articles included in this systematic review. The case series highlighted the presence of stable and clinically unremarkable neutrophilia during a follow-up ranging from one to ten years. Conclusions: Existing evidence indicates that leukocytosis associated with clozapine treatment can be considered as an asymptomatic and benign condition, suggesting that no change in clozapine treatment is needed upon its detection.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with đź’™ for researchers
Part of the Research Solutions Family.