Background eConsulta is a tele-consultation service involving doctors and patients, and is part of Catalonia's public health information technology system. The service has been in operation since the end of 2015 as an adjunct to face-to-face consultations. A key factor in understanding the barriers and facilitators to the acceptance of the tool is understanding the sociodemographic characteristics of general practitioners who determine its use. Objective This study aimed to analyze the sociodemographic factors that affect the likelihood of doctors using eConsulta. Methods A retrospective cross-sectional analysis of administrative data was used to perform a multivariate logistic regression analysis on the use of eConsulta in relation to sociodemographic variables. Results The model shows that the doctors who use eConsulta are 45-54 years of age, score higher than the 80th percentile on the quality of care index, have a high degree of accessibility, are involved in teaching, and work on a health team in a high socioeconomic urban setting. Conclusions The results suggest that certain sociodemographic characteristics associated with general practitioners determine whether they use eConsulta. These results must be taken into account if its deployment is to be encouraged in the context of a public health system.

BACKGROUND During lockdown due to COVID-19 pandemic, telemedicine has become a necessary component of clinical practice for the purpose of providing safer patient care, and it has been used to support the healthcare needs of COVID-19 patients and routine primary care patients alike. However, this change has not been fully consolidated. OBJECTIVE The objective of this study was to analyse the determinants of healthcare professionals’ intention to use the eConsulta digital clinical consultations tool in the post-COVID-19 context. METHODS A mixed qualitative and quantitative methodology was used, and a questionnaire was designed to serve as the data collection instrument. The data were analysed using univariate and bivariate analysis techniques. To confirm the theoretical model, exploratory factor analysis and binary logistic regression were applied. RESULTS The most important variables were those referring to perceived benefits (B=2.408) and the type of use that individuals habitually made of eConsulta (B=0.715). Environmental pressure (B=0.678), experience of technology (B=0.542), gender (B=0.639) and the degree of eConsulta implementation (B=0.266) were other variables influencing the intention to use the tool in the post-COVID-19 context. When replicating the previous analysis by professional group, experience of technology and gender in the physician group, and experience of the tool’s use and the centre where a professional works in the nurse group, were found to be of considerable importance. CONCLUSIONS The implementation and use of eConsulta had increased significantly as a consequence of the COVID-19 pandemic, and the majority of the healthcare professionals were satisfied with its use in practice and planned to incorporate it into their practices in the post-COVID-19 context. Perceived benefits and environmental pressure were determining factors in the attitude towards and intention to use eConsulta.

No abstract

Background Diabetic retinopathy (DR) is one of the most important causes of blindness worldwide, especially in developed countries. In diabetic patients, periodic examination of the back of the eye using a nonmydriatic camera has been widely demonstrated to be an effective system to control and prevent the onset of DR. Convolutional neural networks have been used to detect DR, achieving very high sensitivities and specificities. Objective The objective of this is paper was to develop an artificial intelligence (AI) algorithm for the detection of signs of DR in diabetic patients and to scientifically validate the algorithm to be used as a screening tool in primary care. Methods Under this project, 2 studies will be conducted in a concomitant way: (1) Development of an algorithm with AI to detect signs of DR in patients with diabetes and (2) A prospective study comparing the diagnostic capacity of the AI algorithm with respect to the actual system of family physicians evaluating the images. The standard reference to compare with will be a blinded double reading conducted by retina specialists. For the development of the AI algorithm, different iterations and workouts will be performed on the same set of data. Before starting each new workout, the strategy of dividing the set date into 2 groups will be used randomly. A group with 80% of the images will be used during the training (training dataset), and the remaining 20% images will be used to validate the results (validation dataset) of each cycle (epoch). During the prospective study, true-positive, true-negative, false-positive, and false-negative values will be calculated again. From here, we will obtain the resulting confusion matrix and other indicators to measure the performance of the algorithm. Results Cession of the images began at the end of 2018. The development of the AI algorithm is calculated to last about 3 to 4 months. Inclusion of patients in the cohort will start in early 2019 and is expected to last 3 to 4 months. Preliminary results are expected to be published by the end of 2019. Conclusions The study will allow the development of an algorithm based on AI that can demonstrate an equal or superior performance, and that constitutes a complement or an alternative, to the current screening of DR in diabetic patients. International Registered Report Identifier (IRRID) PRR1-10.2196/12539

BACKGROUND Over the last decade telemedicine services have been introduced in the public healthcare systems of industrialized countries. In Catalonia, the use of eConsulta, an asynchronous teleconsultation between primary care professionals and citizens in the public healthcare system has already reached one million cases. Before the COVID-19 pandemic was growing at a monthly rate of 7%, and the growth has been exponential from march 15th until now. Despite its widespread usage, there is little qualitative evidence describing how this tool is used. OBJECTIVE To annotate a random sample of these teleconsultations and to evaluate the level of agreement between healthcare professionals with respect to the annotation. METHODS 20 General Practitioners retrospectively annotated a random sample of 5,382 cases managed with eConsulta according to 3 variables: 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. 1,217 cases were classified three times, by three different professionals, to assess the degree of consensus among them. RESULTS In response to the question “Has the online consultation avoided a face-to-face visit?”, GPs answered Yes for 79,6% (4,284/5,382) of the teleconsultations, while to the question “In the absence of a service like eConsulta, would the patient have made a face-to-face visit?” GPs answered Yes 65% (3,496/5,382) of the time. The most frequent uses were for management of test results (26.8%, 1,433/5,354), the management of repeat prescriptions (24.3%, 1,301/5,354) and medical enquiries (14.2%, 762/5,354). The degree of agreement among professionals as to the annotations is mixed, with the highest consensus being for the variable “Has the online consultation avoided a face-to-face visit?” (3/3 professionals agreed 68% of the time (827/1,217), and the lowest for the type of use of the teleconsultation (3/3 professionals agreed 57.6% of the time, 701/1,217). CONCLUSIONS This study shows eConsulta’s ability to reduce the number of face-to-face visit stands at between 55% (79% x 65%) and 79% of cases. In comparison to previous research, these results are a bit more pessimistic while figures are still high and in line with administrative data’ proxies, which show 84% of teleconsultations do not register an in-person appointment in the following 3 months. With respect to the type of consultation performed, results are similar to previous literature, thus giving robust support to the eConsulta’s usage. The mixed degree of consensus among professionals implies that results derived from AI applications such as message classification algorithms should be understood in light of these shortcomings. CLINICALTRIAL The study was approved by the Ethical Committee for Clinical Research at the Foundation University Institute for Primary Health Care Research Jordi Gol i Gurina, registration No. P19/096-P.