Task sharing in an interprofessional medication management program – a survey of general practitioners and community pharmacists

Background Pharmacist-led medication review and medication management programs (MMP) are well-known strategies to improve medication safety and effectiveness. If performed interprofessionally, outcomes might even improve. However, little is known about task sharing in interprofessional MMP, in which general practitioners (GPs) and community pharmacists (CPs) collaboratively perform medication reviews and continuously follow-up on patients with designated medical and pharmaceutical tasks, respectively. In 2016, ARMIN (Arzneimittelinitiative Sachsen-Thüringen) an interprofessional MMP was launched in two German federal states, Saxony and Thuringia. The aim of this study was to understand how GPs and CPs share tasks in MMP when reviewing the patients’ medication. Methods This was a cross-sectional postal survey among GPs and CPs who participated in the MMP. Participants were asked who completed which MMP tasks, e.g., checking drug-drug interactions, dosing, and side effects. In total, 15 MMP tasks were surveyed using a 5-point Likert scale ranging from “I complete this task alone” to “GP/CP completes this task alone”. The study was conducted between 11/2020 and 04/2021. Data was analyzed using descriptive statistics. Results In total, 114/165 (69.1%) GPs and 166/243 (68.3%) CPs returned a questionnaire. The majority of GPs and CPs reported (i) checking clinical parameters and medication overuse and underuse to be completed by GPs, (ii) checking storage conditions of drugs and initial compilation of the patient’s medication including brown bag review being mostly performed by CPs, and (iii) checking side-effects, non-adherence, and continuous updating of the medication list were carried out jointly. The responses differed most for problems with self-medication and adding and removing over-the-counter medicines from the medication list. In addition, the responses revealed that some MMP tasks were not sufficiently performed by either GPs or CPs. Conclusions Both GPs’ and CPs’ expertise are needed to perform MMP as comprehensively as possible. Future studies should explore how GPs and CPs can complement each other in MMP most efficiently

A framework to build similarity-based cohorts for personalized treatment advice - a standardized, but flexible workflow with the R package SimBaCo.

Along with increasing amounts of big data sources and increasing computer performance, real-world evidence from such sources likewise gains in importance. While this mostly applies to population averaged results from analyses based on the all available data, it is also possible to conduct so-called personalized analyses based on a data subset whose observations resemble a particular patient for whom a decision is to be made. Claims data from statutory health insurance companies could provide necessary information for such personalized analyses. To derive treatment recommendations from them for a particular patient in everyday care, an automated, reproducible and efficiently programmed workflow would be required. We introduce the R-package SimBaCo (Similarity-Based Cohort generation) offering a simple, but modular, and intuitive framework for this task. With the six built-in R-functions, this framework allows the user to create similarity cohorts tailored to the characteristics of particular patients. An exemplary workflow illustrates the distinct steps beginning with an initial cohort selection according to inclusion and exclusion criteria. A plotting function facilitates investigating a particular patient's characteristics relative to their distribution in a reference cohort, for example the initial cohort or the precision cohort after the data has been trimmed in accordance with chosen variables for similarity finding. Such precision cohorts allow any form of personalized analysis, for example personalized analyses of comparative effectiveness or customized prediction models developed from precision cohorts. In our exemplary workflow, we provide such a treatment comparison whereupon a treatment decision for a particular patient could be made. This is only one field of application where personalized results can directly support the process of clinical reasoning by leveraging information from individual patient data. With this modular package at hand, personalized studies can efficiently weight benefits and risks of treatment options of particular patients