Analysis of Stroke Assistance in Covid-19 Pandemic by Process Mining Techniques

Leandro, Gabrielle dos Santos; Miura, Daniella Yuri; Safanelli, Juliana; Borges, Rafaela Mantoan; Moro, Cláudia Maria Cabral

doi:10.3233/shti220394

Cited by 2 publications

(1 citation statement)

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“…Due to communication challenges among service providers, an increased risk of care gaps accompanies those changes in institutions during treatment and rehabilitation [3]. Therefore, it is essential to monitor the stroke patients' journey during their rehabilitation [4,5].…”

Section: Introductionmentioning

confidence: 99%

‘Quer N0 AVC’ for Monitoring Stroke Patients’ Healthcare Using a Mobile App

Leandro

Bittencourt²,

Nagel³

et al. 2023

Caring Is Sharing – Exploiting the Value in Data for Health and Innovation

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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.

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Section: Introductionmentioning

confidence: 99%

‘Quer N0 AVC’ for Monitoring Stroke Patients’ Healthcare Using a Mobile App

Leandro

Bittencourt²,

Nagel³

et al. 2023

Caring Is Sharing – Exploiting the Value in Data for Health and Innovation

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Process Mining for Social and Economic Needs

Das

Bala

2023

Advances in Human and Social Aspects of Technology

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Process mining is a paradigm shift from traditional process understanding methodologies like interviews and surveys to a data-driven understanding of the actual digital processes. It analyzes business processes by applying algorithms to the event data generated by digital systems. The chapter provides insight into various uses of process mining in different social and economic processes, with examples from past works demonstrating how practical process mining is in detecting and mitigating bottlenecks in these sectors. Then the chapter further delves into the details of process mining algorithms, key features, and metrics that can help practitioners and researchers evaluate process mining for their work. It also highlights some data quality issues in the event log that can inhibit obtaining fair results from process models. Additionally, some current limitations and concerns are described for creating awareness and building over the body of knowledge in the process and sequential mining techniques.

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Analysis of Stroke Assistance in Covid-19 Pandemic by Process Mining Techniques

Cited by 2 publications

References 0 publications

‘Quer N0 AVC’ for Monitoring Stroke Patients’ Healthcare Using a Mobile App

‘Quer N0 AVC’ for Monitoring Stroke Patients’ Healthcare Using a Mobile App

Process Mining for Social and Economic Needs

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