Open Access

Beate Müller, Renate Klaaßen-Mielke, Ana I. González-González, Daniel Grandt, Reinhard Hammerschmidt, Juliane Köberlein-Neu, Petra Kellermann-Mühlhoff, Hans Joachim Trampisch, Till Beckmann, Lara Düvel, Bastian Surmann, Benno Flaig, Peter Ihle, Sara Söling, Simone Grandt, Truc Sophia Dinh, Alexandra Piotrowski, Ingo Meyer, Ute Karbach, Sebastian Harder, Rafael Perera, Paul Glasziou, Holger Pfaff, Wolfgang Greiner, Ferdinand M. Gerlach, Nina Timmesfeld, Christiane Muth

• Introduction: Clinically complex patients often require multiple medications. Polypharmacy is associated with inappropriate prescriptions, which may lead to negative outcomes. Few effective tools are available to help physicians optimise patient medication. This study assesses whether an electronic medication management support system (eMMa) reduces hospitalisation and mortality and improves prescription quality/safety in patients with polypharmacy. Methods and analysis: Planned design: pragmatic, parallel cluster-randomised controlled trial; general practices as randomisation unit; patients as analysis unit. As practice recruitment was poor, we included additional data to our primary endpoint analysis for practices and quarters from October 2017 to March 2021. Since randomisation was performed in waves, final study design corresponds to a stepped-wedge design with open cohort and step-length of one quarter. Scope: general practices, Westphalia-Lippe (Germany), caring for BARMER health fund-covered patients. Population: patients (≥18 years) with polypharmacy (≥5 prescriptions). Sample size: initially, 32 patients from each of 539 practices were required for each study arm (17 200 patients/arm), but only 688 practices were randomised after 2 years of recruitment. Design change ensures that 80% power is nonetheless achieved. Intervention: complex intervention eMMa. Follow-up: at least five quarters/cluster (practice). recruitment: practices recruited/randomised at different times; after follow-up, control group practices may access eMMa. Outcomes: primary endpoint is all-cause mortality and hospitalisation; secondary endpoints are number of potentially inappropriate medications, cause-specific hospitalisation preceded by high-risk prescribing and medication underuse. Statistical analysis: primary and secondary outcomes are measured quarterly at patient level. A generalised linear mixed-effect model and repeated patient measurements are used to consider patient clusters within practices. Time and intervention group are considered fixed factors; variation between practices and patients is fitted as random effects. Intention-to-treat principle is used to analyse primary and key secondary endpoints.