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

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. 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. 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. 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. 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

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

Abstract 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

BackgroundChest 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. ObjectiveThe 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. MethodsA 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. ResultsPatient 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. ConclusionsIf 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)PRR1-10.2196/3953

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

BackgroundVaccination 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. ObjectiveThe 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. MethodsThe 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. ResultsThe 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. ConclusionsThis 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

Analysing Twitter’s Role in Combating the Magnetic Vaccine Conspiracy Theory Using Social Network Analysis

Background: The popularity of the magnetic vaccine conspiracy theory, and others of a similar nature, creates challenges to the promotion of vaccines and the dissemination of accurate health information. Objective: Health conspiracy theories are gaining in popularity. The objective of the study was to evaluate the Twitter social media network related to the magnetic vaccination conspiracy theory and to apply social capital theory to analyse social structures. As a strategy for online public health surveillance, we employ 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 analysed using social network analysis. Data were retrieved from June 01 to June 13 (2021) using the keyword 'vaccine magnetic'. Tweets were retrieved via a dedicated Twitter Application Programming Interface (API). More specifically, the Academic Track API was used, and the data were analysed using NodeXL Pro and Gephi. Results: There were a total of 22,762 connections between Twitter users within the dataset. The study found that the most influential user within the network consisted of a news account that was reporting on the 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 finds that influential users were effective broadcasters against the conspiracy, and their reach extended beyond their own network of Twitter followers. We emphasise the need for trust in contact with influential users concerning health information, particularly in the context of widespread social uncertainty resulting from the pandemic, when public sentiment on social media may be unpredictable. The study highlights the potential of influential users to disrupt information flows of conspiracy theories due to their unique social capital

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

BackgroundThe 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. ObjectiveThis study aimed to analyze the approach to depressive episodes and the role of eHealth in the Catalan health care system from 2017 to 2022. MethodsA 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. ResultsFrom 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). ConclusionsThe 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

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

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

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

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

Acknowledgements: The authors would like to thank all the users who agreed to participate in the study, and the radiology team at the Osona study centre for their help in recruiting patients. We would also like to thank the general practitioners for their participation in the interpretation of the radiologist's reports. Finally, we would also like to thank the professionals at Oxipit for their help in describing the more technical part of the algorithm. In addition, this study was carried out as part of the Industrial Doctorates programme of Catalonia and obtained a Bayès Grant.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