Telemedicine is both effective and able to provide efficient care at a lower cost. It also enjoys a high degree of acceptance among users. The Technology Acceptance Model proposed is based on the two main concepts of ease of use and perceived usefulness and is comprised of three dimensions: the individual context, the technological context and the implementation or organizational context. At present, no short, validated questionnaire exists in Catalonia to evaluate the acceptance of telemedicine services amongst healthcare professionals using a technology acceptance model. This article aims to statistically validate the Catalan version of the EU project Health Optimum telemedicine acceptance questionnaire. The study included the following phases: adaptation and translation of the questionnaire into Catalan and psychometric validation with construct (exploratory factor analysis), consistency (Cronbach’s alpha) and stability (test–retest) analysis. After deleting incomplete responses, calculations were made using 33 participants. The internal consistency measured with the Cronbach’s alpha coefficient was good with an alpha coefficient of 0.84 (95%, CI: 0.79–0.84). The intraclass correlation coefficient was 0.93 (95% CI: 0.852–0.964). The Kaiser–Meyer–Olkin test of sampling showed to be adequate (KMO = 0.818) and the Bartlett test of sphericity was significant (Chi-square 424.188; gl = 28; p < 0.001). The questionnaire had two dimensions which accounted for 61.2% of the total variance: quality and technical difficulties relating to telemedicine. The findings of this study suggest that the validated questionnaire has robust statistical features that make it a good predictive model of healthcare professional’s satisfaction with telemedicine programs.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Tobacco use entails, during pregnancy, a serious risk to the mother and harmful effects on the development of the child. Europa has the highest tobacco smoking prevalence (19.3%) as compared to a worldwide mean of 6.8%. Twenty to thirty percent of pregnant women used tobacco during pregnancy worldwide. These data emphasize the urgent need for community education and implementation of prevention strategies focused on risks associated to tobacco use during pregnancy. Objective: The aim of this study is to investigate the efficacy of an intervention that incorporate a serious game (Tobbstop) to help pregnant smokers to quit smoking. Methods: A two-arm randomized controlled trial enrols 42 pregnant women who were visited in two primary care centres in Catalonia, Spain, between March 2015 and November 2016. All participants were pregnant smokers, over the age of 18 years, attending consultation to a midwife during the first trimester of pregnancy who expressed their desire to stop smoking. Participants were randomized to intervention (n= 21) or to control group (n = 21). Intervention group were instructed to install the game on their smartphone or tablet and use it for 3 months. Until the delivery, all participants were assessed about their stage on smoke cessation during their follow-up midwife consultations. The primary outcome was continuous tobacco abstinence until delivery confirmed by amount of CO at each visit, measured with a carboxymeter. Results: Continuous abstinence until delivery outcome was 57.1% (12/21) in intervention group and 14.3% (3/21) in control group (Hazard Ratio (HR) = 4.31; 95% CI, 1.87-9.97; P = 0.001). Mean of total days without smoking until delivery was higher in intervention group (mean 139.75; SD 21.76) than control group (mean 33.28; SD 13.27; P <.001). Kapplan-Meier survival analysis showed that intervention group has a higher abstinence rate compared with control group (log-rank test, X2 13.91; P <.001). Conclusions: Serious game’s use is associated with an increased likelihood to maintain abstinent during the intervention period compared with those not using the game. Pregnancy is an ideal opportunity to intervene and control tobacco use among future mothers. On the other hand, serious games is an emerging technology, growing in importance, which is shown as a good tool to assist in maintaining women without smoking and help behaviour change during pregnancy. However, due to study design limitations, these outcomes should be interpreted with caution. More research, using larger samples and longer follow-up periods, is needed to replicate the findings of this study. Clinical Trial: Trial Registration: ClinicalTrials.gov NCT01734421; https://clinicaltrials.gov/ct2/show/NCT01734421 (Archived by WebCite at http://www.webcitation.org/75ISc59pB)

Background: The COVID-19 pandemic has turned the care model of health systems around the world upside down, causing the abrupt cancellation of face-to-face visits and redirection of the model toward telemedicine. Digital transformation boosts information systems—the more robust they are, the easier it is to monitor the health care system in a highly complex state and allow for more agile and reliable analysis. Objective: The purpose of this study was to analyze diagnoses from primary care visits and distinguish between those that had higher and lower variations, relative to the 2019 and 2020 periods (roughly before and during COVID-19), to identify clinical profiles that may have been most impaired from the least-used diagnostic codes for visits during the pandemic. Methods: We used a database from the Primary Care Services Information Technologies Information System of Catalonia. We analyzed the register of visits (n=2,824,185) and their International Classification of Diseases (ICD-10) diagnostic codes (n=3,921,974; mean 1.38 per visit), as approximations of the reasons for consultations, at 3 different grouping levels. The data were represented by a term frequency matrix and analyzed recursively in different partitions aggregated according to date. Results: The increase in non–face-to-face visits (+267%) did not counterbalance the decrease in face-to-face visits (–47%), with an overall reduction in the total number of visits of 1.36%, despite the notable increase in nursing visits (10.54%). The largest increases in 2020 were visits with diagnoses related to COVID-19 (ICD-10 codes Z20-Z29: 2.540%), along with codes related to economic and housing problems (ICD-10 codes Z55-Z65: 44.40%). Visits with most of the other diagnostic codes decreased in 2020 relative to those in 2019. The largest reductions were chronic pathologies such as arterial hypertension (ICD-10 codes I10-I16: –32.73%) or diabetes (ICD-10 codes E08-E13: –21.13%), but also obesity (E65-E68: –48.58%) and bodily injuries (ICD-10 code T14: –33.70%). Visits with mental health–related diagnostic codes decreased, but the decrease was less than the average decrease. There was a decrease in consultations—for children, adolescents, and adults—for respiratory infections (ICD-10 codes J00-J06: –40.96%). The results show large year-on-year variations (in absolute terms, an average of 12%), which is representative of the strong shock to the health system. Conclusions: The disruption in the primary care model in Catalonia has led to an explosive increase in the number of non–face-to-face visits. There has been a reduction in the number of visits for diagnoses related to chronic pathologies, respiratory infections, obesity, and bodily injuries. Instead, visits for diagnoses related to socioeconomic and housing problems have increased, which emphasizes the importance of social determinants of health in the context of this pandemic. Big data analytics with routine care data yield findings that are consistent with those derived from intuition in everyday clinical practice and can help inform decision making by health planners in order to use the next few years to focus on the least-treated diseases during the COVID-19 pandemic.

The prevalence of dermatological conditions in primary care, coupled with challenges such as dermatologist shortages and rising consultation costs, highlights the need for innovative solutions. Artificial intelligence (AI) holds promise for improving the diagnostic analysis of skin lesion images, potentially enhancing patient care in primary settings. This systematic review following PRISMA guidelines examined primary studies (2012–2022) assessing AI algorithms’ diagnostic accuracy for skin diseases in primary care. Studies were screened for eligibility based on their availability in the English language and exclusion criteria, with risk of bias evaluated using QUADAS-2. PubMed, Scopus, and Web of Science were searched. Fifteen studies (2019–2022), primarily from Europe and the USA, focusing on diagnostic accuracy were included. Sensitivity ranged from 58% to 96.1%, with accuracies varying from 0.41 to 0.93. AI applications encompassed triage and diagnostic support across diverse skin conditions in primary care settings, involving both patients and primary care professionals. While AI demonstrates potential for enhancing the accuracy of skin disease diagnostics in primary care, further research is imperative to address study heterogeneity and ensure algorithm reliability across diverse populations. Future investigations should prioritise robust dataset development and consider representative patient samples. Overall, AI may improve dermatological diagnosis in primary care, but careful consideration of algorithm limitations and implementation strategies is required.

Background: Artificial Intelligence (AI) has been developing for decades, but in recent years its use in the field of health care has experienced an exponential increase. Currently, there is little doubt that these tools have transformed clinical practice. Therefore, it is important to know how the population perceives its implementation to be able to propose strategies for acceptance and implementation and to improve or prevent problems arising from future applications. Objective: This study aims to describe the population’s perception and knowledge of the use of AI as a health support tool and its application to radiology through a validated questionnaire, in order to develop strategies aimed at increasing acceptance of AI use, reducing possible resistance to change and identifying possible sociodemographic factors related to perception and knowledge. Methods: A cross-sectional observational study was conducted using an anonymous and voluntarily validated questionnaire aimed at the entire population of Catalonia aged 18 years or older. The survey addresses 4 dimensions defined to describe users’ perception of the use of AI in radiology, (1) “distrust and accountability,” (2) “personal interaction,” (3) “efficiency,” and (4) “being informed,” all with questions in a Likert scale format. Results closer to 5 refer to a negative perception of the use of AI, while results closer to 1 express a positive perception. Univariate and bivariate analyses were performed to assess possible associations between the 4 dimensions and sociodemographic characteristics. Results: A total of 379 users responded to the survey, with an average age of 43.9 (SD 17.52) years and 59.8% (n=226) of them identified as female. In addition, 89.8% (n=335) of respondents indicated that they understood the concept of AI. Of the 4 dimensions analyzed, “distrust and accountability” obtained a mean score of 3.37 (SD 0.53), “personal interaction” obtained a mean score of 4.37 (SD 0.60), “efficiency” obtained a mean score of 3.06 (SD 0.73) and “being informed” obtained a mean score of 3.67 (SD 0.57). In relation to the “distrust and accountability” dimension, women, people older than 65 years, the group with university studies, and the population that indicated not understanding the AI concept had significantly more distrust in the use of AI. On the dimension of “being informed,” it was observed that the group with university studies rated access to information more positively and those who indicated not understanding the concept of AI rated it more negatively. Conclusions: The majority of the sample investigated reported being familiar with the concept of AI, with varying degrees of acceptance of its implementation in radiology. It is clear that the most conflictive dimension is “personal interaction,” whereas “efficiency” is where there is the greatest acceptance, being the dimension in which there are the best expectations for the implementation of AI in radiology.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Diabetic retinopathy (DR) is one of the most important causes of blindness worldwide, especially in developed countries. In diabetic patients, examining periodically the back of the eye using a non-mydriatic camera has been widely demonstrated to be an effective system to control and prevent the onset of RD. Convolutional neural networks have been used to detect RD, achieving very high sensitivities and specificities. Objective: ● To develop an artificial intelligence logarithm for the detection of signs of DR in diabetic patients. ● To scientifically validate the logarithm to be used as a screening tool in primary care. Methods: This project will consist in the conduction of two studies in a concomitant way: 1. Development of an algorithm with artificial intelligence to detect signs of DR in patients with diabetes. 2. Prospective study comparing the diagnostic capacity of the algorithm with respect to the actual system of family physicians evaluating the images. The standard reference to compare with will be a double blind reading done by ophthalmologists specialists in retina. For the development of the artificial intelligence algorithm, different iterations and workouts will be performed on the same set of data. Before starting each new workout, the strategy of dividing set date in two groups will be used randomly. A group with 80% of the images, which will be used during the training (training data set) and the other, of the remaining 20% (validation data set) it will be used to validate the results of each cycle (epoch). During the prospective study, the values of correct positive (TP), correct negative (TN), positive (FP), false negative (FN) will be calculated again. From here we will obtain the resulting confusion matrix and other indicators to measure the performance of the algorithm. Results: It is hoped to develop and validate an artificial intelligence system for the detection of DR in diabetic patients. If the results are satisfactory, the developed algorithm can be used as a support tool for family doctors. Conclusions: The study will allow the development of an algorithm based on artificial intelligence that can demonstrate an equal or superior performance, and that constitutes an alternative, to the current screening of RD in diabetic patients.