Revisão terapêutica tipo 1 com base nos registos eletrónicos da farmácia : primeiros passos para o desenho de um algoritmo que permite estratificar doentes para a prestação o de serviços de farmácia personalizados
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2021
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Algoritmos, queries e sistemas baseados em conhecimento são algumas abordagens que permitem ao realizar revisão terapêutica do tipo 1 aos registos eletrónicos de doentes (RED). 0 objetivo deste estudo foi realizar uma revisão terapêutica do tipo 1 numa amostra de RED de uma farmácia comunitária, permitindo a identificação de clusters que sinalizem os doentes que requerem intervenções farmacêuticas. Estudo observacional retrospectivo numa amostra de conveniência de RED. Foram incluídos os RED com histórico de dispensa entre junho de 2017 e julho de 2018 e que apresentavam 2 ou mais medicamentos cr6nicos. Utilizou-se o método de análise de clusters em duas fases para identificar características comuns entre os doentes. Cinquenta e cinco RED foram submetidos a revisão terapêutica tipo 1. A mediana do número de medicamentos usados foi de cinco [IQR: 3,0 — 7,0]. 18,2% dos doentes usavam medicamentos inapropriados e 30,9% possuíam potencial moderado ou grave de interação entre medicamentos. Foram identificados quatro clusters, aos quais foram atribuídas as intervenções farmacêuticas consideradas mais adequadas, bem como a prioridade da sua prestação. A revisão terapêutica aplicada aos RED permitiu a identificação de clusters de doentes, o que, por sua vez, permitira o desenho de um algoritmo baseado em critérios com possibilidade de ser automatizado.
Algorithms, queries, and knowledge-based systems are among approaches to screen electronic patient records stored in databases and support pharmacist medication reviews. The aim of this study was to perform a type 1 medication review and identify clusters that enable the definition of an algorithm to tailor pharmacy professional intery A retrospective observational study was conducted on a convenience sample of pharmacy records. Records were included if patients had a medication dispensing history between June 2017 - July 2018 and used two or more chronic medications. Statistical analysis used a two-step cluster to identify common characteristics among fifty-five sets of patient records which underwent Type 1 medication review. The median number of drugs used per patient was five [IQR: 3.0 — 7.0]. 18.2% of patients had inappropriate drugs, and 30.9% had moderate or major interaction potential. Four clusters were identified based on the variables of interactions, number of drugs used, contraindications, Beers criteria and measurable biomarkers, allowing to envision possible pharmaceutical interventions, as well as the priority in providing that intervention. The identification of patient clusters via medication review of electronic records of pharmacy patients supports the design of criteria-based algorithms, likely to be automated.
Algorithms, queries, and knowledge-based systems are among approaches to screen electronic patient records stored in databases and support pharmacist medication reviews. The aim of this study was to perform a type 1 medication review and identify clusters that enable the definition of an algorithm to tailor pharmacy professional intery A retrospective observational study was conducted on a convenience sample of pharmacy records. Records were included if patients had a medication dispensing history between June 2017 - July 2018 and used two or more chronic medications. Statistical analysis used a two-step cluster to identify common characteristics among fifty-five sets of patient records which underwent Type 1 medication review. The median number of drugs used per patient was five [IQR: 3.0 — 7.0]. 18.2% of patients had inappropriate drugs, and 30.9% had moderate or major interaction potential. Four clusters were identified based on the variables of interactions, number of drugs used, contraindications, Beers criteria and measurable biomarkers, allowing to envision possible pharmaceutical interventions, as well as the priority in providing that intervention. The identification of patient clusters via medication review of electronic records of pharmacy patients supports the design of criteria-based algorithms, likely to be automated.
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Biomedical and biopharmaceutical research : jornal de investigação biomédica e biofarmacêutica
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CIÊNCIAS FARMACÊUTICAS, INFORMAÇÃO CLÍNICA, ALGORITMOS, PHARMACEUTICAL SCIENCES, HEALTH RECORDS, ALGORITHMS, REVISÃO TERAPÊUTICA, MEDICATION REVIEW