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Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining
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. Pr...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5915667/ https://www.ncbi.nlm.nih.gov/pubmed/29636315 http://dx.doi.org/10.2196/jmir.8884 |
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author | Conca, Tania Saint-Pierre, Cecilia Herskovic, Valeria Sepúlveda, Marcos Capurro, Daniel Prieto, Florencia Fernandez-Llatas, Carlos |
author_facet | Conca, Tania Saint-Pierre, Cecilia Herskovic, Valeria Sepúlveda, Marcos Capurro, Daniel Prieto, Florencia Fernandez-Llatas, Carlos |
author_sort | Conca, Tania |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5915667 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-59156672018-05-03 Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining Conca, Tania Saint-Pierre, Cecilia Herskovic, Valeria Sepúlveda, Marcos Capurro, Daniel Prieto, Florencia Fernandez-Llatas, Carlos J Med Internet Res Original Paper 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. JMIR Publications 2018-04-10 /pmc/articles/PMC5915667/ /pubmed/29636315 http://dx.doi.org/10.2196/jmir.8884 Text en ©Tania Conca, Cecilia Saint-Pierre, Valeria Herskovic, Marcos Sepúlveda, Daniel Capurro, Florencia Prieto, Carlos Fernandez-Llatas. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 10.04.2018. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Conca, Tania Saint-Pierre, Cecilia Herskovic, Valeria Sepúlveda, Marcos Capurro, Daniel Prieto, Florencia Fernandez-Llatas, Carlos Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining |
title | Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining |
title_full | Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining |
title_fullStr | Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining |
title_full_unstemmed | Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining |
title_short | Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining |
title_sort | multidisciplinary collaboration in the treatment of patients with type 2 diabetes in primary care: analysis using process mining |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5915667/ https://www.ncbi.nlm.nih.gov/pubmed/29636315 http://dx.doi.org/10.2196/jmir.8884 |
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