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Background: Stroke management requires a coordinated strategy, adhering to clinical pathways (CP) and value-based healthcare (VBHC) principles from onset to rehabilitation. However, the discrepancies between these pathways and actual patient experiences highlight the need for ongoing monitoring and addressing interoperability issues across multiple institutions in stroke care. To address this, the Fast Healthcare Interoperability Resource (FHIR) Implementation Guide (IG) standardizes the information exchange among these systems, considering a specific context of use., Objective: Develop an FHIR IG for stroke care rooted in established stroke CP and VBHC principles., Method: We represented the stroke patient journey by considering the core stroke CP, the International Consortium for Health Outcomes Measurement (ICHOM) dataset for stroke, and a Brazilian case study using the Business Process Model and Notation (BPMN). Next, we developed a data dictionary that aligns variables with existing FHIR resources and adapts profiling from the Brazilian National Health Data Network (BNHDN)., Results: Our BPMN model encompassed three critical phases that represent the entire patient journey from symptom onset to rehabilitation. The stroke data dictionary included 81 variables, which were expressed as questionnaires, profiles, and extensions. The FHIR IG comprised nine pages: Home, Stroke-CP, Data Dictionary, FHIR, ICHOM, Artifacts, Examples, Downloads, and Security. We developed 96 artifacts, including 7 questionnaires, 27 profiles with corresponding example instances, 3 extensions, 18 value sets, and 14 code systems pertinent to ICHOM outcome measures., Conclusion: The FHIR IG for stroke in this study represents a significant advancement in healthcare interoperability, streamlining the tracking of patient outcomes for quality enhancement, facilitating informed treatment choices, and enabling the development of dashboards to promote collaborative excellence in patient care., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

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.

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., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Stroke is one of the leading causes of death and impairments worldwide. After hospital discharge, it is necessary to monitor these patients during their recovery. This research addresses the implementation of a mobile app, entitled 'Quer N0 AVC', to improve the quality of stroke patient care in Joinville, Brazil. The study method was divided into two parts. The adaptation phase included all the necessary information in the app for monitoring stroke patients. The implementation phase aimed to prepare a routine for the Quer mobile app installation. One of the questionnaires collected data from 42 patients and identified that before hospital admission 29% of them did not have medical appointments, 36% had one or two appointments, 11% had three appointments, and 24% had four or more appointments. This research portrayed adaptation feasibility and the implementation of a cell phone app for following up on stroke patients. [ABSTRACT FROM AUTHOR]

• Framework linking Process Mining, Simulation and multicriteria methods in healthcare • Integrates Process Mining and Simulation supports the development of accurate models • Improves the decision-making in the stroke's clinical pathway • Reduces the need for on-site testing during decision-making for process improvements • Supports Decision-making for clinical pathways in an agile manner Health processes are highly complex and sensitive, requiring continued improvement. Several methodologies have been developed to support these improvement processes, many of which apply Process Mining (PM) to investigate the current states of processes. Some even use Discrete Event Simulation (DES) techniques to explore alternative scenarios. Among the ones using DES, some rely on Multicriteria Decision-Making Methods (MCDM) to guide them in choosing the best scenario, especially in complex decision-making environments. However, no author presents a single framework combining all three techniques mentioned above, to wit, PM, DES, and MCDM. This paper targets developing this combination and exploring its usefulness in guiding process improvement in the health services area. The clinical path for stroke patients, covering from onset of symptoms to hospital discharge, was used as a test case. The research demonstrated that this proposal provided an innovative diagnostic of the process, identifying the activities that should be improved to obtain the most significant results in the process management indicators. Identifying the activities ensures that the impacts of these improvements are indicated with statistical accuracy and eliminating the need to perform on-site testing to test for the best solutions. The research does not address root cause diagnosis of activities to be improved. Therefore, further research needs to be undertaken on an extension of the model to enhance root cause analysis capabilities with a view to validating the framework proposed in other environments and, in this way, assess its replicability. [ABSTRACT FROM AUTHOR]