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Open AccessArticle

Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning

1
TIC Salut Social—Ministry of Health, 08028 Barcelona, Spain
2
CRES&CEXS—Pompeu Fabra University, 08003 Barcelona, Spain
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Faculty of Medicine, Barcelona University, 08036 Barcelona, Spain
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IOMED Medical Solutions, 08041 Barcelona, Spain
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Agency for Healthcare Quality and Evaluation of Catalonia (AQuAS), Catalan Ministry of Health, 08005 Barcelona, Spain
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Sant Joan de Déu Hospital, Catalan Ministry of Health, 08950 Barcelona, Spain
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Institut de Biologia Evolutiva (UPF-CSIC), Pompeu Fabra University, 08003 Barcelona, Spain
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Centre d’Atenció Primària Capellades, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, 08786 Sant Fruitós de Bages, Spain
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Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, 08272 Sant Fruitós de Bages, Spain
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Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina, 08272 Sant Fruitós de Bages, Spain
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(3), 1093; https://doi.org/10.3390/ijerph17031093
Received: 15 December 2019 / Revised: 30 January 2020 / Accepted: 7 February 2020 / Published: 9 February 2020
(This article belongs to the Special Issue Social Media and Public Health: Opportunities and Challenges)
Background: The primary care service in Catalonia has operated an asynchronous teleconsulting service between GPs and patients since 2015 (eConsulta), which has generated some 500,000 messages. New developments in big data analysis tools, particularly those involving natural language, can be used to accurately and systematically evaluate the impact of the service. Objective: The study was intended to assess the predictive potential of eConsulta messages through different combinations of vector representation of text and machine learning algorithms and to evaluate their performance. Methodology: Twenty machine learning algorithms (based on five types of algorithms and four text representation techniques) were trained using a sample of 3559 messages (169,102 words) corresponding to 2268 teleconsultations (1.57 messages per teleconsultation) in order to predict the three variables of interest (avoiding the need for a face-to-face visit, increased demand and type of use of the teleconsultation). The performance of the various combinations was measured in terms of precision, sensitivity, F-value and the ROC curve. Results: The best-trained algorithms are generally effective, proving themselves to be more robust when approximating the two binary variables “avoiding the need of a face-to-face visit” and “increased demand” (precision = 0.98 and 0.97, respectively) rather than the variable “type of query” (precision = 0.48). Conclusion: To the best of our knowledge, this study is the first to investigate a machine learning strategy for text classification using primary care teleconsultation datasets. The study illustrates the possible capacities of text analysis using artificial intelligence. The development of a robust text classification tool could be feasible by validating it with more data, making it potentially more useful for decision support for health professionals. View Full-Text
Keywords: machine learning; teleconsultation; primary care; remote consultation; classification machine learning; teleconsultation; primary care; remote consultation; classification
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MDPI and ACS Style

López Seguí, F.; Ander Egg Aguilar, R.; de Maeztu, G.; García-Altés, A.; García Cuyàs, F.; Walsh, S.; Sagarra Castro, M.; Vidal-Alaball, J. Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning. Int. J. Environ. Res. Public Health 2020, 17, 1093. https://doi.org/10.3390/ijerph17031093

AMA Style

López Seguí F, Ander Egg Aguilar R, de Maeztu G, García-Altés A, García Cuyàs F, Walsh S, Sagarra Castro M, Vidal-Alaball J. Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning. International Journal of Environmental Research and Public Health. 2020; 17(3):1093. https://doi.org/10.3390/ijerph17031093

Chicago/Turabian Style

López Seguí, Francesc; Ander Egg Aguilar, Ricardo; de Maeztu, Gabriel; García-Altés, Anna; García Cuyàs, Francesc; Walsh, Sandra; Sagarra Castro, Marta; Vidal-Alaball, Josep. 2020. "Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning" Int. J. Environ. Res. Public Health 17, no. 3: 1093. https://doi.org/10.3390/ijerph17031093

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