Decision-Making Support in Stroke Diagnosis Process: An Approach Based on the PROMETHEE Method and Decision Model Notation.

Decision-making in the field of healthcare is a very complex activity. Several tools have been developed to support the decision-making process. DMN, a modeling technique focused on decisions, is among these and has been gaining prominence in both, literature and business, as has the multi-criteria method PROMETHEE II that helps decision-makers with multi-criteria in analyses. Thus, this research targets combining these two techniques and analyzing the decision support that these two tools afford together. The diagnostic stage of stroke patients was used to perform this work. The research demonstrated that this proposal can drive major gains in efficiency and assertiveness in decision-making in time-sensitive hospital processes. After all, there is a noticeable dearth of hospitals with specialized teams as well as a shortfall of adequate infrastructure for this treatment.

Ischemic stroke: Process perspective, clinical and profile characteristics, and external factors.

OBJECTIVE: To describe a method of analysis for understanding the health care process, enriched with information on the clinical and profile characteristics of the patients. To apply the proposed technique to analyze an ischemic stroke dataset. MATERIALS AND METHODS: We analyzed 4,830 electronic health records (EHRs) from patients with ischemic stroke (2010-2017), containing information about events realized during treatment and clinical and profile information of the patients. The proposed method combined process mining techniques with data analysis, grouping the data by primary care units (PCU - units responsible for the primary care of patients residing in a geographical area). RESULTS: A novel method, named process, data, and management (PDM) analysis method was used for ischemic stroke data and it provided the following outcomes: health care process for patients with ischemic stroke with time statistics; analysis of potential factors for slow hospital admission indicating an increase in the time to hospital admission of 3.4 h (mean value) for patients with an origin at the urgent care center (UCC) - 30% of patients; analysis of PCUs with distinct secondary stroke rates indicating that the social class of patients is the main difference between them; and the visualization of risk factors (before the stroke) by the PCU to inform the health manager about the potential of prevention. DISCUSSION: PDM analysis describes a step-by-step method for combining process analysis with data analysis considering a management focus. The results obtained on the stroke context can support the definition of more refined action plans by the health manager, improving the stroke health care process and preventing new events. CONCLUSION: When a patient is diagnosed with ischemic stroke, immediate treatment is needed. Moreover, it is possible to prevent new events to some degree by monitoring and treating risk factors. PDM analysis provides an overview of the health care process with time, combining elements that affect the treatment flow and factors, which can indicate a potential for preventing new events. We also can apply PDM analysis in different scenarios, when there is information about activities from treatment flow and other characteristics related to the treatment or the prevention of the analyzed disease. The management focus of the results aids in the formulation of service policies, action plans, and resource allocation.