Resumo:
This research proposes the study of a health area process through the modeling of processes and decisions. The modeling of processes and decisions applied to the health area can help in several factors, including the verification of control and traceability points, which reduce the risks to the patient's health and increase the quality of the service provided. The main objective of this research is to describe the logic of an oncology drug treatment process, from end to end, in order to indicate its control points and describe the logic of the decisions made in critical points of this process. The research also presents some specific objectives that are detailed in the introduction. In the theoretical foundation, the terms used during the research are clarified, in order to research the themes related to the modeling of processes and decisions, as well as their relationship in the health area. For the scientific conduct of this research, the modeling method will be used. This paper presents the creation of business process and decision models related to drug treatment in oncology through the use of Business Process Model and Notation and Decision Model and Notation. In addition, it aims to highlight the control points mapped in the process and address them using the 5W1H tool. The model created in BPMN will be exposed to the application of some comprehensibility guidelines, evaluating which ones were followed by the model and the work that was done to adapt the model to comply with others. It is intended to apply a method of verification and validation of diagrammatic models which is still under development and will be detailed during this research. Finally, this work concluded that the BPMN notation was effective in mapping the cancer treatment process, demonstrating the already established control points and the risks at certain points in the process. Also, the application of the DMN notation and its relationship with the BPMN notation was discussed, which showed the difficulty in applying it in processes where the decision has a high level of personalization.
