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.

Effectiveness of an App for tobacco cessation in pregnant smokers (TOBBGEST): study protocol

BACKGROUND

Tobacco consumption during pregnancy is one of the most modifiable causes of morbidity and mortality for both pregnant smokers and their foetus. Even though pregnant smokers are conscious about the negative effects of tobacco consumption, they also had barriers for smoking cessation and most of them continue smoking, being a major public health problem. The aim of this study is to determine the effectiveness of an application (App) for mobile devices, designed with a gamification strategy, in order to help pregnant smokers to quit smoking during pregnancy and in the long term.

METHODS

This study is a multicentre randomized community intervention trial. It will recruit pregnant smokers (200 participants/group), aged more than 18 years, with sporadically or daily smoking habit in the last 30 days and who follow-up their pregnancy in the Sexual and Reproductive Health Care Services of the Camp de Tarragona and Central Catalonia Primary Care Departments. All the participants will have the usual clinical practice intervention for smoking cessation, whereas the intervention group will also have access to the App. The outcome measure will be prolonged abstinence at 12 months after the intervention, as confirmed by expired-carbon monoxide and urinary cotinine tests. Results will be analysed based on intention to treat. Prolonged abstinence rates will be compared, and the determining factors will be evaluated using multivariate statistical analysis.

DISCUSSION

The results of this study will offer evidence about the effectiveness of an intervention using a mobile App in smoking cessation for pregnant smokers, to decrease comorbidity associated with long-term smoking. If this technology is proven effective, it could be readily incorporated into primary care intervention for all pregnant smokers.

TRIAL REGISTRATION

Clinicaltrials.gov ID NCT05222958 . Trial registered 3 February 2022.

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.

Using artificial intelligence as a diagnostic decision support tool in skin disease: observational prospective cohort study (Preprint)

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.

Comparing the Clinical Characteristics and Mortality of Residential and Non-Residential Older People with COVID-19: Retrospective Observational Study

Nursing homes have accounted for a significant part of SARS-CoV-2 mortality, causing great social alarm. Using data collected from electronic medical records of 1,319,839 institutionalised and non-institutionalised persons ≥ 65 years, the present study investigated the epidemiology and differential characteristics between these two population groups. Our results showed that the form of presentation of the epidemic outbreak, as well as some risk factors, are different among the elderly institutionalised population with respect to those who are not. In addition to a twenty-fold increase in the rate of adjusted mortality among institutionalised individuals, the peak incidence was delayed by approximately three weeks. Having dementia was shown to be a risk factor for death, and, unlike the non-institutionalised group, neither obesity nor age were shown to be significantly associated with the risk of death among the institutionalised. These differential characteristics should be able to guide the actions to be taken by the health administration in the event of a similar infectious situation among institutionalised elderly people.

Association between the reduction of face-to-face appointments and the control of patients with type 2 diabetes mellitus during the Covid-19 pandemic in Catalonia

AIM

To analyse the relation between face-to-face appointments and management of patients with type 2 diabetes mellitus (T2DM) visited in primary care practices (PCP).

METHODS

Retrospective study in 287 primary care practices (PCPs) attending>300,000 patients with T2DM. We analysed the results of 9 diabetes-related indicators of the Healthcare quality standard, comprising foot and retinopathy screening, blood pressure (BP) and glycemic control; and the incidence of T2DM. We calculated each indicator's percentage of change in 2020 with respect to the results of 2019.

RESULTS

Indicators' results were reduced in 2020 compared to 2019, highlighting the indicators of foot and retinopathy screening (-51.6% and -25.7%, respectively); the glycemic control indicator (-21.2%); the BP control indicator (-33.7%) and the incidence of T2DM (-25.6%). Conversely, the percentage of type 2 diabetes patients with HbA1c > 10% increased by 34%. PCPs with<11 weekly face-to-face appointments offered per professional had greater reductions than those PCPs with more than 40. For instance, a reduction of -60.7% vs -38.2% (p-value < 0.001) in the foot screening's indicator; -27.5% vs -12.5% (p-value < 0.001) in glycemic control and -40.2 vs -24.3% (p-value < 0.001) in BP control.

CONCLUSIONS

Reducing face-to-face visits offered may impact T2DM patients' follow-up and thus worsen their control.

Assessing the likelihood of contracting COVID-19 disease based on a predictive tree model: A retrospective cohort study

BACKGROUND

Primary care is the major point of access in most health systems in developed countries and therefore for the detection of coronavirus disease 2019 (COVID-19) cases. The quality of its IT systems, together with access to the results of mass screening with Polymerase chain reaction (PCR) tests, makes it possible to analyse the impact of various concurrent factors on the likelihood of contracting the disease.

METHODS AND FINDINGS

Through data mining techniques with the sociodemographic and clinical variables recorded in patient's medical histories, a decision tree-based logistic regression model has been proposed which analyses the significance of demographic and clinical variables in the probability of having a positive PCR in a sample of 7,314 individuals treated in the Primary Care service of the public health system of Catalonia. The statistical approach to decision tree modelling allows 66.2% of diagnoses of infection by COVID-19 to be classified with a sensitivity of 64.3% and a specificity of 62.5%, with prior contact with a positive case being the primary predictor variable.

CONCLUSIONS

The use of a classification tree model may be useful in screening for COVID-19 infection. Contact detection is the most reliable variable for detecting Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases. The model would support that, beyond a symptomatic diagnosis, the best way to detect cases would be to engage in contact tracing.

Diagnosis of Skin Lesions Using Photographs Taken With a Mobile Phone: An Online Survey of Primary Care Physicians

Skin conditions are one of the most frequent reasons for visiting a primary health care facility, making it of vital importance that general practitioners (GPs) have the right knowledge and tools to diagnose the most frequent dermatological conditions. Methods: This study evaluates the accuracy of dermatological diagnoses made by 120 GPs based on photographs taken with a smartphone by an anonymous online cross-sectional survey. Results: The study was carried out between August and October 2018. The results show that the majority of the participants are in favor of using mobile phones to communicate with other professionals and use them to consult medical images. The majority (69%) took dermatological photographs and the preferred device was a smartphone (70%). From 22 different images evaluated, in 69% of responses, participants expressed a high degree of confidence in their ability to diagnose the lesion shown and in 72% of the cases, the diagnosis chosen was correct. Conclusions: The study confirms that the use of smartphone to send medical images is growing rapidly and its potential for taking medical images is an opportunity to help primary care teams deal with dermatological problems. The results suggest that GPs need further training in interpreting dermatological images, to increase their diagnostic confidence and to avoid the need for referrals to face-to-face visits.

A New Tool for Public Health Opinion to Give Insight Into Telemedicine: Twitter Poll Analysis

Background

Telemedicine draws on information technologies in order to enable the delivery of clinical health care from a distance. Twitter is a social networking platform that has 316 million monthly active users with 500 million tweets per day; its potential for real-time monitoring of public health has been well documented. There is a lack of empirical research that has critically examined the potential of Twitter polls for providing insight into public health. One of the benefits of utilizing Twitter polls is that it is possible to gain access to a large audience that can provide instant and real-time feedback. Moreover, Twitter polls are completely anonymized.

Objective

The overall aim of this study was to develop and disseminate Twitter polls based on existing surveys to gain real-time feedback on public views and opinions toward telemedicine.

Methods

Two Twitter polls were developed utilizing questions from previously used questionnaires to explore acceptance of telemedicine among Twitter users. The polls were placed on the Twitter timeline of one of the authors, which had more than 9300 followers, and the account followers were asked to answer the poll and retweet it to reach a larger audience.

Results

In a population where telemedicine was expected to enjoy big support, a significant number of Twitter users responding to the poll felt that telemedicine was not as good as traditional care.

Conclusions

Our results show the potential of Twitter polls for gaining insight into public health topics on a range of health issues not just limited to telemedicine. Our study also sheds light on how Twitter polls can be used to validate and test survey questions.

Exploring Efficacy of a Serious Game (Tobbstop) for Smoking Cessation During Pregnancy: Randomized Controlled Trial

Background

Tobacco use during pregnancy entails a serious risk to the mother and harmful effects on the development of the child. Europe has the highest tobacco smoking prevalence (19.3%) compared with the 6.8% global mean. Between 20% to 30% of pregnant women used tobacco during pregnancy worldwide. These data emphasize the urgent need for community education and implementation of prevention strategies focused on the risks associated with tobacco use during pregnancy.

Objective

The aim of this study was to investigate the efficacy of an intervention that incorporates a serious game (Tobbstop) to help pregnant smokers quit smoking.

Methods

A two-arm randomized controlled trial enrolled 42 women who visited 2 primary care centers in Catalonia, Spain, between March 2015 and November 2016. All participants were pregnant smokers, above 18 years old, attending consultation with a midwife during the first trimester of pregnancy, and had expressed their desire to stop smoking. Participants were randomized to the intervention (n=21) or control group (n=21). The intervention group was instructed to install the game on their mobile phone or tablet and use it for 3 months. Until delivery, all the participants were assessed on their stage of smoking cessation during their follow-up midwife consultations. The primary outcome was continuous tobacco abstinence until delivery confirmed by the amount of carbon monoxide at each visit, measured with a carboxymeter.

Results

Continuous abstinence until delivery outcome was 57% (12/21) in the intervention group versus 14% (3/21) in the control group (hazard ratio=4.31; 95% CI 1.87-9.97; P=.001). The mean of total days without smoking until delivery was higher in the intervention group (mean 139.75, SD 21.76) compared with the control group (mean 33.28, SD 13.27; P<.001). In addition, a Kapplan-Meier survival analysis showed that intervention group has a higher abstinence rate compared with the control group (log-rank test, χ²₁=13.91; P<.001).

Conclusions

Serious game use is associated with an increased likelihood to maintain abstinence during the intervention period if 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 are an emerging technology, growing in importance, which are shown to be a good tool to help quitting smoking during pregnancy and also to maintain this abstinent behavior. However, because of the 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.

Trial Registration

ClinicalTrials.gov NCT01734421; https://clinicaltrials.gov/ct2/show/NCT01734421 (Archived by WebCite at http://www.webcitation.org/75ISc59pB)

Artificial Intelligence for the Detection of Diabetic Retinopathy in Primary Care: Protocol for Algorithm Development

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

Social Networking App Use Among Primary Health Care Professionals: Web-Based Cross-Sectional Survey

Background

Several studies have been conducted to analyze the role social networks play in communication between patients and health professionals. However, there is a shortage of studies in relation to communication among primary health professionals, in a professional context, using the various mobile phone apps available.

Objective

The objective of our study was to explore mobile phone social networking app use among primary health care professionals for work-related purposes, by comparing the most widely used apps in the market.

Methods

We undertook a cross-sectional study using an anonymous Web survey among a convenience sample of 1635 primary health care professionals during August and September 2017.

Results

Of 483 participants in the survey, 474 (98.1%, 95% CI 97.1%-99.4%) were health professionals who commonly accessed social networking sites and 362 (74.9%, 95% CI 71.1%-78.8%) accessed the sites in a work-related context. Of those 362 respondents, 219 (96.7%, 95% CI 94.8%-98.5%) preferred WhatsApp for both personal and professional uses. Of the 362 respondents who used social networking sites in a work-related context, 276 (76.2%, 95% CI 71.9%-80.6%) rated social networking sites as useful or very useful to solve clinical problems, 261 (72.1%, 95% CI 67.5%-76.7%) to improve their professional knowledge, and 254 (70.2%, 95% CI 65.5%-74.9%) to speed up the transmission of clinical information. Most of them (338/362, 94.8%, 95% CI 92.5%-97.0%) used social networking sites for interprofessional communications, and 204 of 362 (56.4%, 95% CI 51.2%-61.5%) used them for pharmacological-related consultations.

Conclusions

Health professionals frequently accessed social networking sites using their mobile phones and often for work-related issues. This trend suggests that social networking sites may be useful tools in primary care settings, but we need to ensure the security of the data transfer process to make sure that social networking sites are used appropriately. Health institutions need to increase information and training activities to ensure the correct use of these tools.

Attending home care patients in primary care using a smartphone application (WhatsICS): A feasibility study

Background: Provision of care to patients with chronic diseases at their homes remains a great challenge for modern health care systems. Smartphone applications are indicated as one of the strategies that could improve care delivery to this group of patients. The aim of this study is to investigate the feasibility and usability of a proprietary application with a messaging service used by a primary care team attending chronic patients mainly at their homes. Methods: A Cross-sectional pilot study of a smartphone application to communicate amongst clinicians. Primary care practices in Tona, Spain, were recruited during a period from January to December 2016. Clinicians used WhatsICS to communicate during their home visits for 12 months. We studied the patterns of use, response time and types of communication. To explore barriers and enablers, semi-structured interviews were conducted with selected nurses, social worker and general practitioners. Results: Two nurses, two practitioners and a social worker were recruited and more than 1,000 hours of communication were recorded on 163 patients, generating 5820 communication events. Nurses initiated the majority of communications (59.79%); these communications were mainly for the purpose of receiving instructions from practitioners and for coordination (66.6%). The communications were made on weekdays, from Monday to Friday, and between 7:30 a.m. and 9:30 p.m. (99.73%). Participants felt that WhatsICS helped streamline and improve home-based care. Conclusions: WhatsICS is safe technologically and accepted as a communication tool for professionals. This study establishes the basis for future implementations of this tool to improve the care of chronic patients at home through smartphones.