Type 1 medication review based on a pharmacy’s electronic medication records: first steps towards an algorithm to stratify patients for tailored pharmacy services

Abstract

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 interv 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.