Abstract
Background and Aims
Diabetes self-management requires high cognitive effort and many daily actions by patients. Decision support systems (DSS) can simplify this process. However, integration of connected devices increases system complexity, justifying the need of dedicated development tools. Existing physiological simulators support the design of closed loop control algorithms but lack description of user-device interaction especially in non-closed-loop settings. Here we present a simulation approach designed to support the development of DSS with increasing complexity.
Methods
To describe realistic behavior, we integrated domain knowledge of device characteristics, patient behavior and environmental factors. By making use of probabilistic modeling techniques, simulation is not limited to pre-defined situations but covers a wider range of alternative scenarios.
Results
We developed a modular simulation environment that allows a holistic description of diabetes therapy self-management as the interactions between the patient, devices, DSS and the environment. We relied on qualitative research to describe therapy tasks as a series of atomic actions. Each task was modelled using Markov chains. Conflicts with other goals were implicitly modelled by varying chain state transition probabilities, each of which are functions of the environmental context, physiological state and the patient’s technical therapy setup.
Conclusions
Our novel simulation environment extends physiologic models with a holistic description of interaction between patient and decision support system. The simulation environment blends behavioral and physiological aspects which allows us to validate prototypical solutions in-silico early on.
