Teleconsultation Between Patients and Health Care Professionals in the Catalan Primary Care Service: Message Annotation Analysis in a Retrospective Cross-Sectional Study.

BACKGROUND: Over the last decade, telemedicine services have been introduced in the public health care systems of several industrialized countries. In Catalonia, the use of eConsulta, an asynchronous teleconsultation service between primary care professionals and citizens in the public health care system, has already reached 1 million cases. Before the COVID-19 pandemic, the use of eConsulta was growing at a monthly rate of 7%, and the growth has been exponential from March 15, 2020 to the present day. Despite its widespread usage, there is little qualitative evidence describing how this tool is used. OBJECTIVE: The aim of this study was to annotate a random sample of teleconsultations from eConsulta, and to evaluate the level of agreement between health care professionals with respect to the annotation. METHODS: Twenty general practitioners retrospectively annotated a random sample of 5382 cases managed by eConsulta according to three aspects: the type of interaction according to 6 author-proposed categories, whether the practitioners believed a face-to-face visit was avoided, and whether they believed the patient would have requested a face-to-face visit had eConsulta not been available. A total of 1217 cases were classified three times by three different professionals to assess the degree of consensus among them. RESULTS: The general practitioners considered that 79.60% (4284/5382) of the teleconsultations resulted in avoiding a face-to-face visit, and considered that 64.96% (3496/5382) of the time, the patient would have made a face-to-face visit in the absence of a service like eConsulta. The most frequent uses were for management of test results (26.77%, 1433/5354), management of repeat prescriptions (24.30%, 1301/5354), and medical enquiries (14.23%, 762/5354). The degree of agreement among professionals as to the annotations was mixed, with the highest consensus demonstrated for the question "Has the online consultation avoided a face-to-face visit?" (3/3 professionals agreed 67.95% of the time, 827/1217), and the lowest consensus for the type of use of the teleconsultation (3/3 professionals agreed 57.60% of the time, 701/1217). CONCLUSIONS: This study shows the ability of eConsulta to reduce the number of face-to-face visits for 55% (79% × 65%) to 79% of cases. In comparison to previous research, these results are slightly more pessimistic, although the rates are still high and in line with administrative data proxies, showing that 84% of patients using teleconsultations do not make an in-person appointment in the following 3 months. With respect to the type of consultation performed, our results are similar to the existing literature, thus providing robust support for eConsulta's usage. The mixed degree of consensus among professionals implies that results derived from artificial intelligence tools such as message classification algorithms should be interpreted in light of these shortcomings.

General Practitioners' Perceptions of Whether Teleconsultations Reduce the Number of Face-to-face Visits in the Catalan Public Primary Care System: Retrospective Cross-Sectional Study.

BACKGROUND: eConsulta is a teleconsultation service involving general practitioners (GPs) and patients. It is part of the information system belonging to Catalonia's primary care service. It has been in operation since the end of 2015 in conjunction with face-to-face consultations with Primary Care Teams as one of the services offered in the patient's Personal Health Folder. OBJECTIVE: This study aimed to assess the ability of using eConsulta to reduce the number of face-to-face visits to Primary Care Teams. METHODS: Using 13 categories proposed by the researchers, 18 GPs from the Central Catalonia Health Region retrospectively classified 2268 cases managed with eConsulta and indicated whether, in their opinion, the teleconsultations reduced the number of face-to-face visits. RESULTS: There was broad consensus among the GPs that eConsulta has the potential to resolve patient queries for every type of consultation. eConsulta avoided the need for a face-to-face visit in 87.9% of cases. In addition, the GPs reported that the ease of access increased the demand for health care support in 27.7% of cases; otherwise, the patient would not have initiated the queries. Therefore, based on the equation (88% x [1-28%]), eConsulta could replace 63%-88% of conventional appointments. The most frequent uses of the teleconsultation service were for management of test results (35.2%), medical enquiries (16.0%), and the management of repeat prescriptions (12.2%). On average, the teleconsultations consisted of a mean 1.57 messages (SD 0.54 messages); 45.9% (1040/2268) of the teleconsultations consisted of 1 message, and the majority of the remaining teleconsultations consisted of 2-5 interactions. The patient initiated 60.0% (1361/2268) of the teleconsultations. CONCLUSIONS: Based on the GPs' perceptions, eConsulta could replace 63%-88% of conventional appointments. Therefore, asynchronous teleconsultations between practitioners and patients in primary care could avoid interactions that have limited added clinical value.

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

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.