Developing and testing a discrete event simulation model to evaluate budget impacts of diabetes prevention programs.

Type 2 diabetes (T2D) is one of the most rapidly increasing non-communicable diseases worldwide. Lifestyle interventions are effective in preventing T2D but also resource intensive. This study evaluated with discrete event simulation (DES) the relative budget impacts of three hypothetical diabetes prevention programs (DPP), including group-based contact intervention, digital program with human coaching and fully automated program. The data for simulation were derived from research literature and national health and population statistics. The model was constructed using the iGrafx Process for Six Sigma software and simulations were carried out for 10 years. All simulated interventions produced cost savings compared to the situation without any intervention. However, this was a modeling study and future studies are needed to verify the results in real-life. Decision makers could benefit the predictive models regarding the long-term effects of diabetes prevention interventions, but more data is needed in particular on the usage, acceptability, effectiveness and costs of digital intervention tools.

A conceptual framework for automating the operational and strategic decision-making process in the health care delivery system.

Making reliable and justified operational and strategic decisions is a really challenging task in the health care domain. So far, the decisions have been made based on the experience of managers and staff, or they are evaluated with traditional methods, using inadequate data. As a result of this kind of decision-making process, attempts to improve operations usually have failed or led to only local improvements. Health care organizations have a lot of operational data, in addition to clinical data, which is the key element for making reliable and justified decisions. However, it is progressively problematic to access it and make usage of it. In this paper we discuss about the possibilities how to exploit operational data in the most efficient way in the decision-making process. We'll share our future visions and propose a conceptual framework for automating the decision-making process.