Medication trajectories visualization among patients with type 2 diabetes

Abstract

Multimorbidity and polypharmacy are strongly linked to diabetes. Multimorbidity complicates prescription choices because of the various drug combinations. Aiming to optimize drug prescription choices in multimorbidity, a first step is to characterize, visualize and understand the current trajectories of treatment patterns among these patients. The overall objective is to visualize longitudinal medication trajectories among patients with type 2 diabetes. The aim is to generate a visualisation that describes the complexity of these trajectories, while limited in size. The research aims to inform the optimization of drug prescription choices by providing information on common prescriptions, their sequence and time intervals. Medication history vectors, per patient, were designed as a sequence containing all BNF chapters of chronic prescriptions. Clustering these vectors identified 11 groups of common trajectory sequences. All clusters contain similar medications but show differences over time. This implies that there’re 11 common trajectories which contain the same BNF chapters but their sequences over time differ. Overall a visualisation was reached that proved useful to visualize medication trajectories over time, while capturing as much complexity possible.