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

Francesc López Seguí, Ricardo Ander Egg Aguilar, Gabriel de Maeztu, Anna García-Altés, Francesc García Cuyàs, Sandra Walsh, Marta Sagarra Castro and Josep Vidal-Alaball
Additional contact information
Francesc López Seguí: TIC Salut Social—Ministry of Health, 08028 Barcelona, Spain
Ricardo Ander Egg Aguilar: Faculty of Medicine, Barcelona University, 08036 Barcelona, Spain
Gabriel de Maeztu: IOMED Medical Solutions, 08041 Barcelona, Spain
Anna García-Altés: Agency for Healthcare Quality and Evaluation of Catalonia (AQuAS), Catalan Ministry of Health, 08005 Barcelona, Spain
Francesc García Cuyàs: Sant Joan de Déu Hospital, Catalan Ministry of Health, 08950 Barcelona, Spain
Sandra Walsh: Institut de Biologia Evolutiva (UPF-CSIC), Pompeu Fabra University, 08003 Barcelona, Spain
Marta Sagarra Castro: Centre d’Atenció Primària Capellades, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, 08786 Sant Fruitós de Bages, Spain
Josep Vidal-Alaball: Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Institut Català de la Salut, 08272 Sant Fruitós de Bages, Spain

IJERPH, 2020, vol. 17, issue 3, 1-9

Abstract: 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.

Keywords: machine learning; teleconsultation; primary care; remote consultation; classification (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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