ORCID Profile
0000-0002-2819-5597
Current Organisations
Karolinska Institutet
,
Universitat Politècnica de València
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Publisher: JMIR Publications Inc.
Date: 09-2017
Abstract: ublic health in several countries is characterized by a shortage of professionals and a lack of economic resources. Monitoring and redesigning processes can foster the success of health care institutions, enabling them to provide a quality service while simultaneously reducing costs. Process mining, a discipline that extracts knowledge from information system data to analyze operational processes, affords an opportunity to understand health care processes. ealth care processes are highly flexible and multidisciplinary, and health care professionals are able to coordinate in a variety of different ways to treat a diagnosis. The aim of this work was to understand whether the ways in which professionals coordinate their work affect the clinical outcome of patients. his paper proposes a method based on the use of process mining to identify patterns of collaboration between physician, nurse, and dietitian in the treatment of patients with type 2 diabetes mellitus and to compare these patterns with the clinical evolution of the patients within the context of primary care. Clustering is used as part of the preprocessing of data to manage the variability, and then process mining is used to identify patterns that may arise. he method is applied in three primary health care centers in Santiago, Chile. A total of seven collaboration patterns were identified, which differed primarily in terms of the number of disciplines present, the participation intensity of each discipline, and the referrals between disciplines. The pattern in which the three disciplines participated in the most equitable and comprehensive manner had a lower proportion of highly decompensated patients compared with those patterns in which the three disciplines participated in an unbalanced manner. y discovering which collaboration patterns lead to improved outcomes, health care centers can promote the most successful patterns among their professionals so as to improve the treatment of patients. Process mining techniques are useful for discovering those collaborations patterns in flexible and unstructured health care processes.
Publisher: MDPI AG
Date: 21-03-2017
DOI: 10.3390/APP7030302
Publisher: Springer New York
Date: 05-11-2014
DOI: 10.1007/978-1-4939-1985-7_10
Abstract: m-Health services are increasing its presence in our lives due to the high penetration of new smartphone devices. This new scenario proposes new challenges in terms of information accessibility that require new paradigms which enable the new applications to access the data in a continuous and ubiquitous way, ensuring the privacy required depending on the kind of data accessed. This paper proposes an architecture based on cloud computing paradigms in order to empower new m-Health applications to enrich their results by providing secure access to user data.
Publisher: JMIR Publications Inc.
Date: 10-04-2018
DOI: 10.2196/JMIR.8884
Publisher: MDPI AG
Date: 21-09-2012
DOI: 10.3390/FI4030852
Publisher: IEEE
Date: 05-2011
DOI: 10.1109/AMS.2011.22
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: JMIR Publications Inc.
Date: 09-2017
Publisher: IEEE
Date: 08-2011
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 03-2011
Publisher: MDPI AG
Date: 20-05-2019
Abstract: The application of Value-based Healthcare requires not only the identification of key processes in the clinical domain but also an adequate analysis of the value chain delivered to the patient. Data Science and Big Data approaches are technologies that enable the creation of accurate systems that model reality. However, classical Data Mining techniques are presented by professionals as black boxes. This evokes a lack of trust in those techniques in the medical domain. Process Mining technologies are human-understandable Data Science tools that can fill this gap to support the application of Value-Based Healthcare in real domains. The aim of this paper is to perform an analysis of the ways in which Process Mining techniques can support health professionals in the application of Value-Based Technologies. For this purpose, we explored these techniques by analyzing emergency processes and applying the critical timing of Stroke treatment and a Question-Driven methodology. To demonstrate the possibilities of Process Mining in the characterization of the emergency process, we used a real log with 9046 emergency episodes from 2145 stroke patients that occurred from January 2010 to June 2017. Our results demonstrate how Process Mining technology can highlight the differences between the flow of stroke patients compared with that of other patients in an emergency. Further, we show that support for health professionals can be provided by improving their understanding of these techniques and enhancing the quality of care.
No related grants have been discovered for Carlos Fernandez-Llatas.