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Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining

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Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining

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dc.contributor.author Conca, Tania es_ES
dc.contributor.author Saint Pierre, Cecilia es_ES
dc.contributor.author Herskovic, Valeria es_ES
dc.contributor.author Sepulveda, Marcos es_ES
dc.contributor.author Capurro, Daniel es_ES
dc.contributor.author Prieto, Florencia es_ES
dc.contributor.author Fernández Llatas, Carlos es_ES
dc.date.accessioned 2020-09-12T03:33:59Z
dc.date.available 2020-09-12T03:33:59Z
dc.date.issued 2018-04 es_ES
dc.identifier.issn 1438-8871 es_ES
dc.identifier.uri http://hdl.handle.net/10251/149918
dc.description.abstract [EN] Background: Public 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. Objective: Health 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. Methods: This 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. Results: The 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. Conclusions: By 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. es_ES
dc.description.sponsorship This paper was partially funded by the National Commission for Scientific and Technological Research, the Formation of Advanced Human Capital Program and the National Fund for Scientific and Technological Development (CONICYT-PCHA/Doctorado Nacional/2016-21161705 and CONICYT-FONDECYT/1150365; Chile). The authors would like to thank Ancora UC primary health care centers for their help with this research. The founding sponsors had no role in the design of the study in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. es_ES
dc.language Inglés es_ES
dc.publisher JMIR Publications Inc. es_ES
dc.relation.ispartof JOURNAL OF MEDICAL INTERNET RESEARCH es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Process assessment (health care) es_ES
dc.subject Interprofessional relations es_ES
dc.subject Primary health care es_ES
dc.subject Type 2 diabetes mellitus es_ES
dc.subject Data mining es_ES
dc.title Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.2196/jmir.8884 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/CONICYT//2016-21161705/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/CONICYT//1150365/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/FONDECYT//1150365/ es_ES
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Conca, T.; Saint Pierre, C.; Herskovic, V.; Sepulveda, M.; Capurro, D.; Prieto, F.; Fernández Llatas, C. (2018). Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining. JOURNAL OF MEDICAL INTERNET RESEARCH. 20(4). https://doi.org/10.2196/jmir.8884 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.2196/jmir.8884 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 20 es_ES
dc.description.issue 4 es_ES
dc.identifier.pmid 29636315 es_ES
dc.identifier.pmcid PMC5915667 es_ES
dc.relation.pasarela S\385997 es_ES
dc.contributor.funder Fondo Nacional de Desarrollo Científico y Tecnológico, Chile es_ES
dc.contributor.funder Comisión Nacional de Investigación Científica y Tecnológica, Chile es_ES
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