The County of Stockholm (Sweden), like any country or region, experiences a continuous need to handle the healthcare necessities of various patient groups. Each group can be seen as a subpopulation, with its own distinctive traits, characteristics, and challenges. The discussed simulation project focused on the dialysis patients, a group who needs to visit caregiving facilities frequently.
When the simulation model was originally created, the major focus was to get a better understanding of the consequences of having several decentralized caregivers, compared to more compact centralization. Most models are enhanced and further developed to handle new ideas and needs, and so the current model also looked at handling different patients in different ways (some handle their need in their homes, some visit a caregiver, some need assistance, and some do not).
This problem was seen as a macro problem and was best handled with a multimethod modeling approach. Agent-based modeling was used to model the environment (the county) and the patients, while discrete event modeling was used to model caregivers and simple caregiving processes. The visualization played a vital role in supporting understanding.
In this case, a number of conclusions could be drawn even before running the model through the animation and visualization. Given the scenario described in the input data (provided through a number of Excel files, and the changes made possible with various interactive controls in the model interface), the "center of gravity" was visually shown – both from the need (patient) perspective and from the capacity (caregiver). If these centers were close to each other, the situation was statically sound. The simulation then helped the modelers understand if this was also the case dynamically, over time.
In most modeling situations, and especially in healthcare, the value added by models is given through a combination of quantitative consequence indicators (figures), and a more general understanding of the issue – which is of a more qualitative type. In this case, examples of indicators were achieved care production (and knowledge of whether it was sufficient), travelling distances for patients, and the utilization of resources, which resulted in a better understanding of whether the capacity was acceptable.
Qualitative outcomes allowed modelers to:
- Drastically raise the understanding of the whole challenge among decision makers and stakeholders.
- Add the geographical dimension and perspective to the whole issue (which is often forgotten).
- Act as a catalyst to support and raise the level of discussions, and make conflicting stakeholders understand that the issue was complex.
- Summarize relevant perspectives, dimensions, and challenges visually and dynamically.
The qualitative aspects usually contribute even more than the quantitative ones (enabling a better decision making process), even though the quantitative output has a higher quality than what could be obtained through other means.
This model is currently being used as a decision-support tool by the specialists involved in addressing the challenges of the dialysis care in the County of Stockholm.