Impact of a New Gynecologic Oncology Hashtag During Virtual-Only ASCO Annual Meetings: An X (Twitter) Social Network Analysis.

Background: Official conference hashtags are commonly used to promote tweeting and social media engagement. The reach and impact of introducing a new hashtag during an oncology conference have yet to be studied. The American Society of Clinical Oncology (ASCO) conducts an annual global meeting, which was entirely virtual due to the COVID-19 pandemic in 2020 and 2021. Objective: This study aimed to assess the reach and impact (in the form of vertices and edges generated) and X (formerly Twitter) activity of the new hashtags #goASCO20 and #goASCO21 in the ASCO 2020 and 2021 virtual conferences. Methods: New hashtags (#goASCO20 and #goASCO21) were created for the ASCO virtual conferences in 2020 and 2021 to help focus gynecologic oncology discussion at the ASCO meetings. Data were retrieved using these hashtags (#goASCO20 for 2020 and #goASCO21 for 2021). A social network analysis was performed using the NodeXL software application. Results: The hashtags #goASCO20 and #goASCO21 had similar impacts on the social network. Analysis of the reach and impact of the individual hashtags found #goASCO20 to have 150 vertices and 2519 total edges and #goASCO20 to have 174 vertices and 2062 total edges. Mentions and tweets between 2020 and 2021 were also similar. The circles representing different users were spatially arranged in a more balanced way in 2021. Tweets using the #goASCO21 hashtag received significantly more responses than tweets using #goASCO20 (75 times in 2020 vs 360 times in 2021; z value=16.63 and P<.001). This indicates increased engagement in the subsequent year. Conclusions: Introducing a gynecologic oncology specialty-specific hashtag (#goASCO20 and #goASCO21) that is related but different from the official conference hashtag (#ASCO20 and #ASCO21) helped facilitate discussion on topics of interest to gynecologic oncologists during a virtual pan-oncology meeting. This impact was visible in the social network analysis.

The Use of Artificial Intelligence for Skin Disease Diagnosis in Primary Care Settings: A Systematic Review.

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.

Correction: Feasibility of a Pediatric Acute Video Consultation Process Among Health Care Professionals in Primary Care in a Rural Setting: Protocol for a Prospective Validation Study.

[This corrects the article DOI: 10.2196/52946.].

Real-world testing of an artificial intelligence algorithm for the analysis of chest X-rays in primary care settings.

Interpreting chest X-rays is a complex task, and artificial intelligence algorithms for this purpose are currently being developed. It is important to perform external validations of these algorithms in order to implement them. This study therefore aims to externally validate an AI algorithm's diagnoses in real clinical practice, comparing them to a radiologist's diagnoses. The aim is also to identify diagnoses the algorithm may not have been trained for. A prospective observational study for the external validation of the AI algorithm in a region of Catalonia, comparing the AI algorithm's diagnosis with that of the reference radiologist, considered the gold standard. The external validation was performed with a sample of 278 images and reports, 51.8% of which showed no radiological abnormalities according to the radiologist's report. Analysing the validity of the AI algorithm, the average accuracy was 0.95 (95% CI 0.92; 0.98), the sensitivity was 0.48 (95% CI 0.30; 0.66) and the specificity was 0.98 (95% CI 0.97; 0.99). The conditions where the algorithm was most sensitive were external, upper abdominal and cardiac and/or valvular implants. On the other hand, the conditions where the algorithm was less sensitive were in the mediastinum, vessels and bone. The algorithm has been validated in the primary care setting and has proven to be useful when identifying images with or without conditions. However, in order to be a valuable tool to help and support experts, it requires additional real-world training to enhance its diagnostic capabilities for some of the conditions analysed. Our study emphasizes the need for continuous improvement to ensure the algorithm's effectiveness in primary care.

Impact of COVID-19 on the Pediatric Primary Care Model in Catalonia: Analysis of Changes in the Number and Type of Face-to-Face and Remote Visits.

Background: The outbreak of COVID-19 has turned the care model of health systems around the world upside down. The health care crisis has led to opportunities for digital health to deliver quality care, and the system has been redirected toward telemedicine. In Catalonia, Spain, as of March 2020, the pattern of visits in primary care pediatric consultations changed, such that face-to-face visits decreased in favor of non-face-to-face visits. Objective: This study aimed to analyze variations in the types of pediatric visits in primary care centers in Catalonia before and after the onset of COVID-19. Methods: This was a descriptive observational study based on administrative data. The number and type of visits to primary care pediatric services in Catalonia between January 2019 and December 2022 were studied. Results: A drop of more than 80% in face-to-face visits and an increase of up to 15 times in remote visits were observed as of March 2020 compared to the previous year. Subsequently, the face-to-face attendance rate began to recover, although it did not reach the same rate as before COVID-19. Non-face-to-face visits were maintained, representing more than 20% of the total after more than 2 years of the pandemic. Conclusions: COVID-19 has been the trigger for a transition in the types of visits to primary care pediatric services. The COVID-19 pandemic was a clear catalyst for the integration of telemedicine in Catalan pediatric health care. In this context, although face-to-face consultations have recovered in absolute numbers, after the pandemic period, the weight of telemedicine has increased.

A Sex- and Gender-Based Approach to Chronic Conditions in Central Catalonia (Spain): A Descriptive Cross-Sectional Study.

The growth of chronic conditions worldwide poses a challenge for both health systems and the quality of life of people with these conditions. However, sex- and gender-based approaches are scarce in this field. Adopting this perspective, this study aims to describe the prevalence of chronic conditions in the Bages-Moianès region (Catalonia, Spain), and analyse the associations of chronic conditions with sex and age. This cross-sectional study used data from the population assigned to the Catalan Health Institute primary care settings in this area between 2018 and 2021 (n = 163,024). A total of 26 chronic conditions (grouped into 7 typologies), sex and age were the analysis variables. A total of 75,936 individuals presented at least one chronic condition, representing 46.6% of the analysed population. The prevalence was higher among women and older individuals. Being male was associated with a greater probability of presenting cardiovascular diseases, neurodevelopmental disorders and metabolic diseases and a lower probability of presenting neurodegenerative diseases, chronic pain and mental health disorders. Adjusting by sex, a positive age gradient was observed in most groups, except for respiratory diseases and mental health disorders. Chronic conditions have a high prevalence in the Bages-Moianès region, showing differences in typology, sex and age. Adopting gender perspectives (both in health systems and future research) is crucial when dealing with chronic conditions in order to take into account their differential impact.

Feasibility of a Pediatric Acute Video Consultation Process Among Health Care Professionals in Primary Care in a Rural Setting: Protocol for a Prospective Validation Study.

BACKGROUND: For years, in Catalonia and in the rest of Spain, there has been a deficit and an unequal geographical distribution of health professionals specializing in pediatrics, especially in rural areas. Among the proposals to improve this situation is the promotion of the use of information and communication technologies (ICT) among users and professionals. Moreover, with the outbreak of COVID-19, the use of telehealth has become an essential tool, with an overall increase in non-face-to-face visits, including in primary care pediatrics. In this context, telemedicine, when used in primary care pediatrics, can be an effective means of improving families' access to medical care. Currently, in Catalonia, telemedicine involving patients and health professionals is used in pediatric primary care through telephone consultation and asynchronous teleconsultation (eConsulta). Video consultation is in practice not used, although it could have different applications. OBJECTIVE: The aim of this study is to evaluate the feasibility of a video consultation process with physical examination in acute pediatric pathology in rural areas among primary care professionals. In addition, the level of satisfaction with these remote consultations will be assessed from the perspective of both the users and the health care professionals. METHODS: We will conduct a prospective experimental study to analyze the possibility of using video consultation in pediatric acute care in primary care in central Catalonia (Spain). A minimum of 170 children aged between 0 and 14 years attending the primary care center (PCC) for acute illness for a period of 1 year will be included in the study. Initially, the telemetric visit, including a physical examination, will include a nurse at the patient and family's side and a pediatrician who will participate remotely. Subsequently, the pediatrician will visit the patient in person and the physical examination and diagnosis made during the remote visit will be compared with the physical examination and diagnosis of the face-to-face visit, which is considered the gold standard. RESULTS: Recruitment was planned to begin in the second half of 2023 and continue for at least 1 year. It is anticipated to be a good resource for a variety of acute pediatric conditions in primary care. The evaluation will focus on the feasibility of performing live remote visits and comparing their diagnostic accuracy with that of face-to-face visits. CONCLUSIONS: We believe that this study could provide evidence on the feasibility and diagnostic accuracy of video consultation in pediatric acute primary care in a rural setting, as well as on satisfaction with video consultations among both users and professionals. If proven useful in addressing the acute needs of children in a variety of situations, it could become a digital health tool that improves the overall pediatric primary care service in rural areas, for both families and professionals. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/52946.

eHealth in the Management of Depressive Episodes in Catalonia's Primary Care From 2017 to 2022: Retrospective Observational Study.

BACKGROUND: The reasons for mental health consultations are becoming increasingly relevant in primary care. The Catalan health care system is undergoing a process of digital transformation, where eHealth is becoming increasingly relevant in routine clinical practice. OBJECTIVE: This study aimed to analyze the approach to depressive episodes and the role of eHealth in the Catalan health care system from 2017 to 2022. METHODS: A retrospective observational study was conducted on diagnostic codes related to depressive episodes and mood disorders between 2017 and 2022 using data from the Catalan Institute of Health. The sociodemographic evolution and prevalence of depression and mood disorders in Catalonia were analyzed between 2017 and 2022. Sociodemographic variables were analyzed using absolute frequency and percentage. The prevalence of depressive episodes was calculated, highlighting the year-to-year changes. The use of eHealth for related consultations was assessed by comparing the percentages of eHealth and face-to-face consultations. A comparison of sociodemographic variables based on attendance type was conducted. Additionally, a logistic regression model was used to explore factors influencing face-to-face attendance. The analysis used R software (version 4.2.1), with all differences examined using 95% CIs. RESULTS: From 2017 to 2022, there was an 86.6% increase in the prevalence of depression and mood disorders, with women consistently more affected (20,950/31,197, 67.2% in 2017 and 22,078/33,169, 66.6% in 2022). In 2022, a significant rise in depression diagnoses was observed in rural areas (difference 0.71%, 95% CI 0.04%-1.43%), contrasting with a significant decrease in urban settings (difference -0.7%, 95% CI -1.35% to -0.05%). There was a significant increase in antidepressant use in 2022 compared to 2017 (difference 2.4%, 95% CI 1.87%-3.06%) and the proportion of eHealth visits rose from 4.34% (1240/28,561) in 2017 to 26.3% (8501/32,267) in 2022. Logistic regression analysis indicated that men (odds ratio [OR] 1.06, 95% CI 1.04-1.09) and younger individuals had a higher likelihood of eHealth consultations in 2022. Furthermore, individuals using eHealth consultations were more likely to use antidepressants (OR 1.54, 95% CI 1.50-1.57) and anxiolytics (OR 1.06, 95% CI 1.03-1.09). CONCLUSIONS: The prevalence of depression in Catalonia has significantly increased in the last 6 years, likely influenced by the COVID-19 pandemic. Despite ongoing digital transformation since 2011, eHealth usage remained limited as of 2017. During the lockdown period, eHealth accounted for nearly half of all health care consultations, representing a quarter of consultations by 2022. In the immediate aftermath of the COVID-19 pandemic, emerging evidence suggests a significant role of eHealth in managing depression-related consultations, along with an apparent likelihood of patients being prescribed antidepressants and anxiolytics. Further research is needed to understand the long-term impact of eHealth on diagnostic practices and medication use.

Knowledge and Perception of the Use of AI and its Implementation in the Field of Radiology: Cross-Sectional Study.

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.

Knowledge and perception of primary care healthcare professionals on the use of artificial intelligence as a healthcare tool.

Objective: The rapid digitisation of healthcare data and the sheer volume being generated means that artificial intelligence (AI) is becoming a new reality in the practice of medicine. For this reason, describing the perception of primary care (PC) healthcare professionals on the use of AI as a healthcare tool and its impact in radiology is crucial to ensure its successful implementation. Methods: Observational cross-sectional study, using the validated Shinners Artificial Intelligence Perception survey, aimed at all PC medical and nursing professionals in the health region of Central Catalonia. Results: The survey was sent to 1068 health professionals, of whom 301 responded. And 85.7% indicated that they understood the concept of AI but there were discrepancies in the use of this tool; 65.8% indicated that they had not received any AI training and 91.4% that they would like to receive training. The mean score for the professional impact of AI was 3.62 points out of 5 (standard deviation (SD) = 0.72), with a higher score among practitioners who had some prior knowledge of and interest in AI. The mean score for preparedness for AI was 2.76 points out of 5 (SD = 0.70), with higher scores for nursing and those who use or do not know if they use AI. Conclusions: The results of this study show that the majority of professionals understood the concept of AI, perceived its impact positively, and felt prepared for its implementation. In addition, despite being limited to a diagnostic aid, the implementation of AI in radiology was a high priority for these professionals.

Acceptance or rejection of vaccination against influenza and SARS-CoV2 viruses among primary care professionals in Central Catalonia. A cross-sectional study.

Background: With the outbreak of the SARS-CoV-2 pandemic, the uncertainty about the real impact of coinfection with other viruses, and the increased risk of mortality in the case of coinfection with the influenza virus, health authorities recommended an increase in influenza vaccination coverage among at-risk groups to minimize the possible impact on individuals and the healthcare system. Recommendations for influenza vaccination during the 2020-2021 campaign in Catalonia were focused on increasing vaccination coverage, especially for social and healthcare workers, elderly people and at-risk individuals of any age. The objectives for the 2020-2021 season in Catalonia were to reach 75 % for the elderly and for social and healthcare workers, and 60 % for pregnant women and at-risk groups. In the case of healthcare professionals and those over 65 years of age, the target was not met. Vaccination coverage reached 65.58 % and 66.44 %, respectively (in the 2019-2020 campaign it was 39.08 %).Analysing and following up on the background and context in which health professionals accept influenza vaccination will help develop strategies for long-term influenza vaccination campaigns. The present study looks at healthcare professionals in a specific territory where the reasons for acceptance or refusal of the influenza vaccine during the 2021-2022 vaccination campaign, as well as the reasons for acceptance or refusal of the COVID-19 vaccine, were analysed by means of an online survey. Methods: Calculations suggested that a random sample of 290 individuals would be sufficient to estimate, with 95% confidence and a precision of +/- 5 percentage units, a population percentage that was expected to be around 30%. The required replacement rate was 10%.The R statistical software (version 3.6.3) was used for the statistical analysis. Confidence intervals were 95 % and contrasts with a p-value of < 0.05 were considered significant. Findings: Of the 1921 professionals to whom the survey was sent, 586 (30.5%) responded to all the questions. 95.2% of respondents were vaccinated against COVID-19 and 66.2% against influenza.It was observed that the relationship between sociodemographic characteristics and the decision to get vaccinated was different for influenza and COVID-19. The reasons for accepting the COVID-19 vaccine with the highest percentage were firstly protecting family (82.2%), self-protection (74.9%) and also protecting patients (57.8%). Otherwise, other reasons not described in the survey (50%) and mistrust (42.3%) were the reasons for rejecting the COVID-19 vaccine.Regarding influenza, the most relevant reasons for which professionals got vaccinated were self-protection (70.7%), protecting family (69.7%) and protecting patients (58.4%). Reasons for refusing the influenza vaccine were reasons not mentioned in the survey (29.1%) and the low probability of suffering complications (27.4%). Interpretation: Analysing the context, territory, sector, and the reasons for both accepting and refusing a vaccine will help develop effective strategies. Although vaccination coverage against COVID-19 was very high throughout Spain, a marked increase in influenza vaccination in the context of COVID-19 was observed among healthcare professionals in the Central Catalonia region compared to the previous pre-pandemic campaign.

Twitter's Role in Combating the Magnetic Vaccine Conspiracy Theory: Social Network Analysis of Tweets.

BACKGROUND: The popularity of the magnetic vaccine conspiracy theory and other conspiracy theories of a similar nature creates challenges to promoting vaccines and disseminating accurate health information. OBJECTIVE: Health conspiracy theories are gaining in popularity. This study's objective was to evaluate the Twitter social media network related to the magnetic vaccine conspiracy theory and apply social capital theory to analyze the unique social structures of influential users. As a strategy for web-based public health surveillance, we conducted a social network analysis to identify the important opinion leaders sharing the conspiracy, the key websites, and the narratives. METHODS: A total of 18,706 tweets were retrieved and analyzed by using social network analysis. Data were retrieved from June 1 to June 13, 2021, using the keyword vaccine magnetic. Tweets were retrieved via a dedicated Twitter application programming interface. More specifically, the Academic Track application programming interface was used, and the data were analyzed by using NodeXL Pro (Social Media Research Foundation) and Gephi. RESULTS: There were a total of 22,762 connections between Twitter users within the data set. This study found that the most influential user within the network consisted of a news account that was reporting on the magnetic vaccine conspiracy. There were also several other users that became influential, such as an epidemiologist, a health economist, and a retired sports athlete who exerted their social capital within the network. CONCLUSIONS: Our study found that influential users were effective broadcasters against the conspiracy, and their reach extended beyond their own networks of Twitter followers. We emphasize the need for trust in influential users with regard to health information, particularly in the context of the widespread social uncertainty resulting from the COVID-19 pandemic, when public sentiment on social media may be unpredictable. This study highlights the potential of influential users to disrupt information flows of conspiracy theories via their unique social capital.

Exploring the potential of artificial intelligence in improving skin lesion diagnosis in primary care.

Dermatological conditions are a relevant health problem. Machine learning (ML) models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, especially for skin cancer detection and disease classification. The objective of this study was to perform a prospective validation of an image analysis ML model, which is capable of screening 44 skin diseases, comparing its diagnostic accuracy with that of General Practitioners (GPs) and teledermatology (TD) dermatologists in a real-life setting. Prospective, diagnostic accuracy study including 100 consecutive patients with a skin problem who visited a participating GP in central Catalonia, Spain, between June 2021 and October 2021. The skin issue was first assessed by the GPs. Then an anonymised skin disease picture was taken and uploaded to the ML application, which returned a list with the Top-5 possible diagnosis in order of probability. The same image was then sent to a dermatologist via TD for diagnosis, as per clinical practice. The GPs Top-3, ML model's Top-5 and dermatologist's Top-3 assessments were compared to calculate the accuracy, sensitivity, specificity and diagnostic accuracy of the ML models. The overall Top-1 accuracy of the ML model (39%) was lower than that of GPs (64%) and dermatologists (72%). When the analysis was limited to the diagnoses on which the algorithm had been explicitly trained (n = 82), the balanced Top-1 accuracy of the ML model increased (48%) and in the Top-3 (75%) was comparable to the GPs Top-3 accuracy (76%). The Top-5 accuracy of the ML model (89%) was comparable to the dermatologist Top-3 accuracy (90%). For the different diseases, the sensitivity of the model (Top-3 87% and Top-5 96%) is higher than that of the clinicians (Top-3 GPs 76% and Top-3 dermatologists 84%) only in the benign tumour pathology group, being on the other hand the most prevalent category (n = 53). About the satisfaction of professionals, 92% of the GPs considered it as a useful diagnostic support tool (DST) for the differential diagnosis and in 60% of the cases as an aid in the final diagnosis of the skin lesion. The overall diagnostic accuracy of the model in this study, under real-life conditions, is lower than that of both GPs and dermatologists. This result aligns with the findings of few existing prospective studies conducted under real-life conditions. The outcomes emphasize the significance of involving clinicians in the training of the model and the capability of ML models to assist GPs, particularly in differential diagnosis. Nevertheless, external testing in real-life conditions is crucial for data validation and regulation of these AI diagnostic models before they can be used in primary care.

Detection of gender-based violence in primary care in Central Catalonia: a descriptive cross-sectional study.

BACKGROUND: Violence against women is a serious public health problem. Primary care could be one of the ideal places for the detection of gender-based violence (GBV), since women come into contact with PC at some point in their lives to look after their sexual and reproductive health. The increase in initiatives promoted by the health authorities regarding GBV offers the possibility of observing its evolution over the last few years. METHODS: A descriptive cross-sectional study of reported cases of GBV in the region of Central Catalonia, during the period from 2017 to 2021, was carried out. All women of legal age, belonging to the specified health region and suffering episodes of GBV, were included. The variables analysed were age, area of residence, health diagnoses related to GBV, whether or not they were pregnant at the time of the attack, and mental health history. RESULTS: Of the total number of women studied, 1,467 presented some type of diagnosis of GBV, with a total of 3,452 episodes reported. We found an increase in the detection of cases, although it must be noted that there is an underreporting of cases in PC. The prevalence according to the total number of women assigned per year over the period studied was 0.42% in 2017 and 0.48% in 2021. It has also been observed that the average number of episodes per woman increased from 1.03 in 2017 to 1.15 in 2021. During the 5 years analysed, the minimum number of episodes per woman was 1 and the maximum was 10. In reference to the duration of the episodes, the minimum was 1 day, and the maximum was 32 years. The mean age of the women was 42.10 years, the most frequent nationality was Spanish (46.60%), and 54.15% of them lived in rural areas. CONCLUSIONS: Despite the established protocols and procedures, it seems that primary health care is not the most frequent place for its detection. It is necessary to continue working to raise awareness and train professionals, and to ensure coordination among all the parties involved in accompanying women in these processes. TRIAL REGISTRATION: CEIm: 21/278-P.

Developing an Artificial Intelligence Model for Reading Chest X-rays: Protocol for a Prospective Validation Study.

BACKGROUND: Chest x-rays are the most commonly used type of x-rays today, accounting for up to 26% of all radiographic tests performed. However, chest radiography is a complex imaging modality to interpret. Several studies have reported discrepancies in chest x-ray interpretations among emergency physicians and radiologists. It is of vital importance to be able to offer a fast and reliable diagnosis for this kind of x-ray, using artificial intelligence (AI) to support the clinician. Oxipit has developed an AI algorithm for reading chest x-rays, available through a web platform called ChestEye. This platform is an automatic computer-aided diagnosis system where a reading of the inserted chest x-ray is performed, and an automatic report is returned with a capacity to detect 75 pathologies, covering 90% of diagnoses. OBJECTIVE: The overall objective of the study is to perform validation with prospective data of the ChestEye algorithm as a diagnostic aid. We wish to validate the algorithm for a single pathology and multiple pathologies by evaluating the accuracy, sensitivity, and specificity of the algorithm. METHODS: A prospective validation study will be carried out to compare the diagnosis of the reference radiologists for the users attending the primary care center in the Osona region (Spain), with the diagnosis of the ChestEye AI algorithm. Anonymized chest x-ray images will be acquired and fed into the AI algorithm interface, which will return an automatic report. A radiologist will evaluate the same chest x-ray, and both assessments will be compared to calculate the precision, sensitivity, specificity, and accuracy of the AI algorithm. Results will be represented globally and individually for each pathology using a confusion matrix and the One-vs-All methodology. RESULTS: Patient recruitment was conducted from February 7, 2022, and it is expected that data can be obtained in 5 to 6 months. In June 2022, more than 450 x-rays have been collected, so it is expected that 600 samples will be gathered in July 2022. We hope to obtain sufficient evidence to demonstrate that the use of AI in the reading of chest x-rays can be a good tool for diagnostic support. However, there is a decreasing number of radiology professionals and, therefore, it is necessary to develop and validate tools to support professionals who have to interpret these tests. CONCLUSIONS: If the results of the validation of the model are satisfactory, it could be implemented as a support tool and allow an increase in the accuracy and speed of diagnosis, patient safety, and agility in the primary care system, while reducing the cost of unnecessary tests. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/39536.

The #VaccinesWork Hashtag on Twitter in the Context of the COVID-19 Pandemic: Network Analysis.

BACKGROUND: Vaccination is one of the most successful public health interventions for the prevention of COVID-19. Toward the end of April 2021, UNICEF (United Nations International Children's Emergency Fund), alongside other organizations, were promoting the hashtag #VaccinesWork. OBJECTIVE: The aim of this paper is to analyze the #VaccinesWork hashtag on Twitter in the context of the COVID-19 pandemic, analyzing the main messages shared and the organizations involved. METHODS: The data set used in this study consists of 11,085 tweets containing the #VaccinesWork hashtag from the 29th to the 30th of April 2021. The data set includes tweets that may not have the hashtag but were replies or mentions in those tweets. The data were retrieved using NodeXL, and the network graph was laid out using the Harel-Koren fast multiscale layout algorithm. RESULTS: The study found that organizations such as the World Health Organization, UNICEF, and Gavi were the key opinion leaders and had a big influence on the spread of information among users. Furthermore, the most shared URLs belonged to academic journals with a high impact factor. Provaccination users had other vaccination-promoting hashtags in common, not only in the COVID-19 scenario. CONCLUSIONS: This study investigated the discussions surrounding the #VaccinesWork hashtag. Social media networks containing conspiracy theories tend to contain dubious accounts leading the discussions and are often linked to unverified information. This kind of analysis can be useful to detect the optimal moment for launching health campaigns on Twitter.

An Artificial Intelligence-Driven Digital Health Solution to Support Clinical Management of Patients With Long COVID-19: Protocol for a Prospective Multicenter Observational Study.

BACKGROUND: COVID-19 pandemic has revealed the weaknesses of most health systems around the world, collapsing them and depleting their available health care resources. Fortunately, the development and enforcement of specific public health policies, such as vaccination, mask wearing, and social distancing, among others, has reduced the prevalence and complications associated with COVID-19 in its acute phase. However, the aftermath of the global pandemic has called for an efficient approach to manage patients with long COVID-19. This is a great opportunity to leverage on innovative digital health solutions to provide exhausted health care systems with the most cost-effective and efficient tools available to support the clinical management of this population. In this context, the SENSING-AI project is focused on the research toward the implementation of an artificial intelligence-driven digital health solution that supports both the adaptive self-management of people living with long COVID-19 and the health care staff in charge of the management and follow-up of this population. OBJECTIVE: The objective of this protocol is the prospective collection of psychometric and biometric data from 10 patients for training algorithms and prediction models to complement the SENSING-AI cohort. METHODS: Publicly available health and lifestyle data registries will be consulted and complemented with a retrospective cohort of anonymized data collected from clinical information of patients diagnosed with long COVID-19. Furthermore, a prospective patient-generated data set will be captured using wearable devices and validated patient-reported outcomes questionnaires to complement the retrospective cohort. Finally, the 'Findability, Accessibility, Interoperability, and Reuse' guiding principles for scientific data management and stewardship will be applied to the resulting data set to encourage the continuous process of discovery, evaluation, and reuse of information for the research community at large. RESULTS: The SENSING-AI cohort is expected to be completed during 2022. It is expected that sufficient data will be obtained to generate artificial intelligence models based on behavior change and mental well-being techniques to improve patients' self-management, while providing useful and timely clinical decision support services to health care professionals based on risk stratification models and early detection of exacerbations. CONCLUSIONS: SENSING-AI focuses on obtaining high-quality data of patients with long COVID-19 during their daily life. Supporting these patients is of paramount importance in the current pandemic situation, including supporting their health care professionals in a cost-effective and efficient management of long COVID-19. TRIAL REGISTRATION: Clinicaltrials.gov NCT05204615; https://clinicaltrials.gov/ct2/show/NCT05204615. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37704.

Asynchronous Remote Communication as a Tool for Care Management in Primary Care: A Rapid Review of the Literature.

Aim: To review the available evidence on asynchronous communication models between primary care professionals and patients in different countries around the world in order to analyse the added value that this model brings to patients and professionals. Design: A rapid literature review was conducted using the World Health Organisation guidance to include a variety of studies on the concept of asynchronous communications between primary care and patients in different countries. Data sources: The search for articles was carried out in PubMed and Google Academics and with the contribution of telemedicine experts from the Catalan Institute of Health. Selection of studies: The review included 271 articles. The inclusion criteria were: publications from 2010 onwards, in English, Spanish or Catalan, focused on asynchronous communications between primary care professionals and patients to improve patient management. After discarding duplicates and applying the exclusion criteria (255 articles), 16 were included for further review. Data extraction: The rapid literature review was conducted by an evaluator; detecting 5 main general themes: reduction of face-to-face visits, available services and most frequent uses, characteristics and perceptions of primary care professionals, characteristics and perceptions of users, and barriers and facilitators for the implementation of asynchronous teleconsultation. Results: A total of sixteen studies were included, including seven quantitative studies, seven qualitative studies and two mixed studies. Conclusions: The high degree of satisfaction of both users and professionals, the outbreak of COVID-19 and the effectiveness and efficiency of asynchronous remote communications are key factors for the implementation and improvement in the management of the different healthcare systems across the world.

Using Artificial Intelligence as a Diagnostic Decision Support Tool in Skin Disease: Protocol for an Observational Prospective Cohort Study.

BACKGROUND: Dermatological conditions are a relevant health problem. Each person has an average of 1.6 skin diseases per year, and consultations for skin pathology represent 20% of the total annual visits to primary care and around 35% are referred to a dermatology specialist. Machine learning (ML) models can be a good tool to help primary care professionals, as it can analyze and optimize complex sets of data. In addition, ML models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, especially for skin cancer detection and classification. OBJECTIVE: This study aims to perform a prospective validation of an image analysis ML model as a diagnostic decision support tool for the diagnosis of dermatological conditions. METHODS: In this prospective study, 100 consecutive patients who visit a participant general practitioner (GP) with a skin problem in central Catalonia were recruited. Data collection was planned to last 7 months. Anonymized pictures of skin diseases were taken and introduced to the ML model interface (capable of screening for 44 different skin diseases), which returned the top 5 diagnoses by probability. The same image was also sent as a teledermatology consultation following the current stablished workflow. The GP, ML model, and dermatologist's assessments will be compared to calculate the precision, sensitivity, specificity, and accuracy of the ML model. The results will be represented globally and individually for each skin disease class using a confusion matrix and one-versus-all methodology. The time taken to make the diagnosis will also be taken into consideration. RESULTS: Patient recruitment began in June 2021 and lasted for 5 months. Currently, all patients have been recruited and the images have been shown to the GPs and dermatologists. The analysis of the results has already started. CONCLUSIONS: This study will provide information about ML models' effectiveness and limitations. External testing is essential for regulating these diagnostic systems to deploy ML models in a primary care practice setting.

Influenza vaccination in coronavirus times: Primary Care professionals' intention to get vaccinated in Central Catalonia (VAGCOVID). A cross sectional study.

Influenza vaccination is the main measure of prevention against epidemic flu. Although recommended, vaccination coverage remains low. The lack of knowledge about the evolution of influenza in the context of the SARS-CoV-2 coronavirus pandemic led to the recommendation of influenza vaccination to people at risk and professionals to avoid a greater burden than the one already posed by SARS-CoV-2. The aim of the study is to determine health professionals' intention to vaccinate against seasonal flu in the 2020-2021 campaign, in the context of the SARS-CoV-2 pandemic, and to analyse the factors that influence it. Cross-sectional study through a structured survey aimed at Primary Care professionals in Central Catalonia. A total of 610 participants responded to the survey, 65.7% of whom intended to get vaccinated against influenza in this campaign, and 11.1% did not know or did not answer. The intention to get vaccinated is associated with the professional category and the number of years of professional practice. The profile of the professionals who intend to get vaccinated against flu includes professionals with a history of vaccination, who participate in on-call duties and perceive that their dependents were at risk of becoming ill. During the SARS-CoV-2 pandemic, although almost two-thirds of the respondents showed a clear intention to get vaccinated against influenza, 11% were doubtful. To improve influenza vaccination uptake among health professionals, strategies need to be devised to target those professionals who are hesitant or reluctant to vaccinate.