Can agent-based simulation be used as a tool to support polypharmacy prescribing practice?

Abstract

Objective: We sought to develop a simulation modelling method to help better understand the complex interplay of factors that lead to people with type 2 diabetes and asthma not taking all of their medication as prescribed when faced with multiple medications (polypharmacy).

Research design and methods: In collaboration with polypharmacy patients, general practitioners, pharmacists and polypharmacy researchers, we developed a map of factors that directly and indirectly affect somebody’s decision to take their medication as prescribed when faced with multiple type 2 diabetes and asthma medications. We then translated these behavioural influences into logical rules using data from the literature and developed a proof-of-concept agent-based simulation model that captures the medicine-taking behaviours of those with type 2 diabetes and asthma taking multiple medications and which predicts both the clinical effectiveness and rates of adherence for different combinations of medications.

Conclusions: The model we have developed could be used as a prescription support tool or a way of estimating medicine-taking behaviour in cost-effectiveness analyses.

Keywords: Agent Based Simulation.

Figure 1

Overview of the behavioural logic of the model for each person at each time step. Indirect general factors influence direct general factors, which resolve to a binary proposed action to take the medicine or not to take it. The wellness factor also resolves to a proposed action, depending on the person’s perception of their state of health with their current drug combination. Proposed actions are selected at random according to the weighting attributed to them, and the person will either switch their medication or continue as they are depending on the selected action.