DOI: https://doi.org/10.37349/edht.2023.00001
Transmitted primarily by Aedes aegypti (Ae. aegypti) and Aedes albopictus (Ae. albopictus), arboviral diseases pose a major global public health threat. Dengue, chikungunya, and zika are increasingly prevalent in Southeast Asia. Among other arboviruses, dengue and zika are becoming more common in Central and South America. Given human encroachment into previously uninhabited, often deforested areas, to provide new housing in regions of population expansion, conceptualizing built urban environments in a novel way is urgently needed to safeguard against the growing climate change-driven threat of vector-borne diseases. By understanding the spread from a One Health perspective, enhanced control and prevention can be achieved. This is particularly important considering that climate change is likely to significantly impact the persistence of ponded water where mosquitoes breed due to increasing temperature and shifting rainfall patterns with regard to magnitude, duration, frequency, and season. Models can incorporate aquatic mosquito stages and adult spatial dynamics when habitats are heterogeneously available, thereby including dispersal and susceptible-exposed-infected-recovered (SEIR) epidemiology. Coupled with human population distribution (density, locations), atmospheric conditions (air temperature, precipitation), and hydrological conditions (soil moisture distribution, ponding persistence in topographic depressions), modeling has improved predictive ability for infection rates. However, it has not informed interventional approaches from an urban environment perspective which considers the role of ponds/lakes that support green spaces, the density of population that enables rapid spread of disease, and varying micro-habitats for various mosquito stages under climate change. Here, for an example of dengue in Vietnam, a preventive and predictive approach to design resilient urban environments is proposed, which uses data from rapidly expanding metropolitan communities to learn continually. This protocol deploys computational approaches including simulation and machine learning/artificial intelligence, underpinned by surveillance and medical data for validation and adaptive learning. Its application may best inform urban planning in low-middle income countries in tropical zones where arboviral pathogens are prevalent.
Transmitted primarily by Aedes aegypti (Ae. aegypti) and Aedes albopictus (Ae. albopictus), arboviral diseases pose a major global public health threat. Dengue, chikungunya, and zika are increasingly prevalent in Southeast Asia. Among other arboviruses, dengue and zika are becoming more common in Central and South America. Given human encroachment into previously uninhabited, often deforested areas, to provide new housing in regions of population expansion, conceptualizing built urban environments in a novel way is urgently needed to safeguard against the growing climate change-driven threat of vector-borne diseases. By understanding the spread from a One Health perspective, enhanced control and prevention can be achieved. This is particularly important considering that climate change is likely to significantly impact the persistence of ponded water where mosquitoes breed due to increasing temperature and shifting rainfall patterns with regard to magnitude, duration, frequency, and season. Models can incorporate aquatic mosquito stages and adult spatial dynamics when habitats are heterogeneously available, thereby including dispersal and susceptible-exposed-infected-recovered (SEIR) epidemiology. Coupled with human population distribution (density, locations), atmospheric conditions (air temperature, precipitation), and hydrological conditions (soil moisture distribution, ponding persistence in topographic depressions), modeling has improved predictive ability for infection rates. However, it has not informed interventional approaches from an urban environment perspective which considers the role of ponds/lakes that support green spaces, the density of population that enables rapid spread of disease, and varying micro-habitats for various mosquito stages under climate change. Here, for an example of dengue in Vietnam, a preventive and predictive approach to design resilient urban environments is proposed, which uses data from rapidly expanding metropolitan communities to learn continually. This protocol deploys computational approaches including simulation and machine learning/artificial intelligence, underpinned by surveillance and medical data for validation and adaptive learning. Its application may best inform urban planning in low-middle income countries in tropical zones where arboviral pathogens are prevalent.
DOI: https://doi.org/10.37349/edht.2023.00004
X (formerly Twitter), a microblogging social media platform, is being used by scientists and researchers to disseminate their research findings and promote the visibility of their work to the public. Tweets can be posted with text messages, images, hyperlinks, or a combination of these features. Importantly, for the majority of users, the text must be limited to 280 characters. In this perspective, this study aimed to observe if adding an image is able to increase outreach for scientific communication on X. Therefore, the characteristics of tweets posted with the hashtag #SciComm (short for science communication) for a period of one year (28 May 2020 to 28 May 2021) were analyzed with the X analytics tool Symplur Signals. The conducted analysis revealed that when a science communication (#SciComm-containing) tweet is accompanied by an image added by the user, there is on average a 529% increase in the number of retweets, and adding a hyperlink is similarly effective in increasing the number of retweets. However, combining both an image and hyperlink in the same tweet did not yield an additive effect. Hence, for increased visibility, researchers may consider adding images or hyperlinks (e.g., to research publications or popular science articles) while communicating science to the public on X.
X (formerly Twitter), a microblogging social media platform, is being used by scientists and researchers to disseminate their research findings and promote the visibility of their work to the public. Tweets can be posted with text messages, images, hyperlinks, or a combination of these features. Importantly, for the majority of users, the text must be limited to 280 characters. In this perspective, this study aimed to observe if adding an image is able to increase outreach for scientific communication on X. Therefore, the characteristics of tweets posted with the hashtag #SciComm (short for science communication) for a period of one year (28 May 2020 to 28 May 2021) were analyzed with the X analytics tool Symplur Signals. The conducted analysis revealed that when a science communication (#SciComm-containing) tweet is accompanied by an image added by the user, there is on average a 529% increase in the number of retweets, and adding a hyperlink is similarly effective in increasing the number of retweets. However, combining both an image and hyperlink in the same tweet did not yield an additive effect. Hence, for increased visibility, researchers may consider adding images or hyperlinks (e.g., to research publications or popular science articles) while communicating science to the public on X.
DOI: https://doi.org/10.37349/edht.2023.00005
The use of generative artificial intelligence (AI) chatbots, such as ChatGPT and YouChat, has increased enormously since their release in late 2022. Concerns have been raised over the potential of chatbots to facilitate cheating in education settings, including essay writing and exams. In addition, multiple publishers have updated their editorial policies to prohibit chatbot authorship on publications. This article highlights another potentially concerning issue; the strong propensity of chatbots in response to queries requesting medical and scientific information and its underlying references, to generate plausible looking but inaccurate responses, with the chatbots also generating nonexistent citations. As an example, a number of queries were generated and, using two popular chatbots, demonstrated that both generated inaccurate outputs. The authors thus urge extreme caution, because unwitting application of inconsistent and potentially inaccurate medical information could have adverse outcomes.
The use of generative artificial intelligence (AI) chatbots, such as ChatGPT and YouChat, has increased enormously since their release in late 2022. Concerns have been raised over the potential of chatbots to facilitate cheating in education settings, including essay writing and exams. In addition, multiple publishers have updated their editorial policies to prohibit chatbot authorship on publications. This article highlights another potentially concerning issue; the strong propensity of chatbots in response to queries requesting medical and scientific information and its underlying references, to generate plausible looking but inaccurate responses, with the chatbots also generating nonexistent citations. As an example, a number of queries were generated and, using two popular chatbots, demonstrated that both generated inaccurate outputs. The authors thus urge extreme caution, because unwitting application of inconsistent and potentially inaccurate medical information could have adverse outcomes.
DOI: https://doi.org/10.37349/edht.2024.00006
Aim:
Mobile health applications (MHAs) have been rapidly designed and urgently need evaluation. Existing evaluation methods, such as platform, development, and subjective overall user observations, are mostly based on application (app) design. This study aimed to evaluate the functionality and feasibility of an MHA to train cognitive function in youth at clinical high risk (CHR) for psychosis with a tool that allows a comprehensive user experience evaluation of mobile apps from multiple dimensions.
Methods:
Eighty participants with CHR for psychosis were recruited and randomly assigned to the intervention and the group control. Participants in the intervention group used the Specific Memory Attention Resource and Training (SMART) app for three months. MHA’s functionality and feasibility were measured by the mobile app rating scale (MARS) and qualitative tools.
Results:
Participants in the SMART group report that the form and design of this app are simple to operate, and the content is trustworthy. They reported improvement in cognitive function and more motivation to seek help to improve their cognitive function. They also pointed out areas of improvement.
Conclusions:
SMART usability and functionality were measured by a multidimensional tool. It shows promise in improving CHR memory and attention and demonstrates appropriate usability and functionality.
Aim:
Mobile health applications (MHAs) have been rapidly designed and urgently need evaluation. Existing evaluation methods, such as platform, development, and subjective overall user observations, are mostly based on application (app) design. This study aimed to evaluate the functionality and feasibility of an MHA to train cognitive function in youth at clinical high risk (CHR) for psychosis with a tool that allows a comprehensive user experience evaluation of mobile apps from multiple dimensions.
Methods:
Eighty participants with CHR for psychosis were recruited and randomly assigned to the intervention and the group control. Participants in the intervention group used the Specific Memory Attention Resource and Training (SMART) app for three months. MHA’s functionality and feasibility were measured by the mobile app rating scale (MARS) and qualitative tools.
Results:
Participants in the SMART group report that the form and design of this app are simple to operate, and the content is trustworthy. They reported improvement in cognitive function and more motivation to seek help to improve their cognitive function. They also pointed out areas of improvement.
Conclusions:
SMART usability and functionality were measured by a multidimensional tool. It shows promise in improving CHR memory and attention and demonstrates appropriate usability and functionality.
DOI: https://doi.org/10.37349/edht.2024.00007
Aim:
After the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and the realization of mass vaccination against the virus, the availability of a reliable, rapid, and easy-to-use system for registering the individual anti-S1 antibody titer could facilitate the personalized assessment of the need for booster vaccine doses and the reduction of social distancing and other measures.
Methods:
The biosensor system is based on immobilized engineered SK-N-SH neuroblastoma cells, bearing the S1 protein, and it can detect immunoglobulin G (IgG) antibodies against the SARS-CoV-2 S1 spike antigen. A disposable electrode strip bearing the engineered mammalian cells is connected to a customized read-out potentiometric device with real-time data transmission to a wireless fidelity (WiFi)-connected smartphone. Blood samples from past-infected individuals and individuals vaccinated against SARS-CoV-2 were used for validation.
Results:
In the present study, a smartphone application (app), capable of analyzing data regarding the levels of anti-S1 antibodies in blood is introduced. The app works in conjunction with a portable, ultra-rapid, and sensitive biosensor transmitting real-time measurements to the smartphone. Both historical and current individual data can be encoded by using the app, resulting in a widely accepted quick response (QR) code, which can then be constantly updated to match a person’s status.
Conclusions:
This novel system could be utilized for the eventual development of a coronavirus disease 2019 (COVID-19) electronic passport, which could be further employed to improve the population-wide, cross-country surveillance of vaccination efficiency, as well as facilitate the implementation of cross-border digital health services in a user-friendly and secure way.
Aim:
After the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and the realization of mass vaccination against the virus, the availability of a reliable, rapid, and easy-to-use system for registering the individual anti-S1 antibody titer could facilitate the personalized assessment of the need for booster vaccine doses and the reduction of social distancing and other measures.
Methods:
The biosensor system is based on immobilized engineered SK-N-SH neuroblastoma cells, bearing the S1 protein, and it can detect immunoglobulin G (IgG) antibodies against the SARS-CoV-2 S1 spike antigen. A disposable electrode strip bearing the engineered mammalian cells is connected to a customized read-out potentiometric device with real-time data transmission to a wireless fidelity (WiFi)-connected smartphone. Blood samples from past-infected individuals and individuals vaccinated against SARS-CoV-2 were used for validation.
Results:
In the present study, a smartphone application (app), capable of analyzing data regarding the levels of anti-S1 antibodies in blood is introduced. The app works in conjunction with a portable, ultra-rapid, and sensitive biosensor transmitting real-time measurements to the smartphone. Both historical and current individual data can be encoded by using the app, resulting in a widely accepted quick response (QR) code, which can then be constantly updated to match a person’s status.
Conclusions:
This novel system could be utilized for the eventual development of a coronavirus disease 2019 (COVID-19) electronic passport, which could be further employed to improve the population-wide, cross-country surveillance of vaccination efficiency, as well as facilitate the implementation of cross-border digital health services in a user-friendly and secure way.
DOI: https://doi.org/10.37349/edht.2024.00008
This article belongs to the special issue Biosensors for Bioactive Molecules
Aim:
This study investigates the potential of artificial intelligence (AI) in revolutionizing healthcare insurance claim processing in the USA. It aims to determine the most effective machine learning (ML) model for predicting health insurance claims, leading to cost savings for insurance companies.
Methods:
Six ML algorithms were used to predict health insurance claims, and their performance was evaluated using various metrics. The algorithms examined include support vector machine (SVM), decision tree (DT), random forest (RF), linear regression (LR), extreme gradient boosting (XGBoost), and k-nearest neighbors (KNN). The research involves a performance assessment that encompasses key metrics. Additionally, a feature importance analysis is conducted to illuminate the critical variables that exert influence on the prediction of insurance claims.
Results:
The findings demonstrate that the XGBoost and RF models outperformed the other algorithms, displaying the highest R-squared values of 79% and 77% and the lowest prediction errors. The feature importance analysis underscores the pivotal role of variables such as smoking habits, body mass index (BMI), and blood pressure levels in the domain of insurance claim prediction. These results emphasize the degree to which these variables should be included in the formulation of insurance policies and pricing strategies.
Conclusions:
This study supports the transformative potential of AI, with specific emphasis on the XGBoost model, in extending the precision and efficiency of healthcare insurance claim processing. The identification of key variables and the mitigation of prediction errors not only signal the potential for substantial cost savings but also affirm the potential to integrate AI into healthcare insurance processes. This research supports the value of the utilization of AI as an emerging tool for process optimization and data-informed decision-making within the healthcare insurance domain.
Aim:
This study investigates the potential of artificial intelligence (AI) in revolutionizing healthcare insurance claim processing in the USA. It aims to determine the most effective machine learning (ML) model for predicting health insurance claims, leading to cost savings for insurance companies.
Methods:
Six ML algorithms were used to predict health insurance claims, and their performance was evaluated using various metrics. The algorithms examined include support vector machine (SVM), decision tree (DT), random forest (RF), linear regression (LR), extreme gradient boosting (XGBoost), and k-nearest neighbors (KNN). The research involves a performance assessment that encompasses key metrics. Additionally, a feature importance analysis is conducted to illuminate the critical variables that exert influence on the prediction of insurance claims.
Results:
The findings demonstrate that the XGBoost and RF models outperformed the other algorithms, displaying the highest R-squared values of 79% and 77% and the lowest prediction errors. The feature importance analysis underscores the pivotal role of variables such as smoking habits, body mass index (BMI), and blood pressure levels in the domain of insurance claim prediction. These results emphasize the degree to which these variables should be included in the formulation of insurance policies and pricing strategies.
Conclusions:
This study supports the transformative potential of AI, with specific emphasis on the XGBoost model, in extending the precision and efficiency of healthcare insurance claim processing. The identification of key variables and the mitigation of prediction errors not only signal the potential for substantial cost savings but also affirm the potential to integrate AI into healthcare insurance processes. This research supports the value of the utilization of AI as an emerging tool for process optimization and data-informed decision-making within the healthcare insurance domain.
DOI: https://doi.org/10.37349/edht.2024.00009
This article belongs to the special issue Data-informed Decision Making in Healthcare
The paper aimed to provide a comprehensive overview of the use of digital health technologies in the assessment, treatment, and self-management of psychological and psychopathological factors associated with asthma. A collection of research articles and systematic reviews related to asthma, including topics such as outdoor air pollution, early life wheezing illnesses, atopic dermatitis, digital interventions for asthma self-management, psychiatric disorders and asthma, family influences on pediatric asthma, and the use of mobile health (mHealth) applications for asthma management, were analyzed. Eight selected studies were reviewed to assess the potential of digital health technologies in improving asthma psychological-related factors management and treatment outcomes. The reviewed studies suggest that electronic health (eHealth) interventions, mixed reality tools, mHealth technology-enhanced nurse-guided interventions, and smartphone applications integrating Bluetooth-enabled sensors for asthma inhalers can significantly improve symptom self-management, quality of life, and mental health outcomes, especially in children and adolescents with asthma (JMIR Pediatr Parent. 2019;2:e12427. doi: 10.2196/12427; Cochrane Database Syst Rev. 2018;8:CD012489. doi: 10.1002/14651858.CD012489.pub2; Int J Environ Res Public Health. 2020;17:7750. doi: 10.3390/ijerph17217750; J Med Internet Res. 2017;19:e113. doi: 10.2196/jmir.6994; J Med Internet Res. 2021;23:e25472. doi: 10.2196/25472; Ann Allergy Asthma Immunol. 2015;114:341–2.E2. doi: 10.1016/j.anai.2014.12.017; J Med Internet Res. 2022;24:e38030. doi: 10.2196/38030; Int J Qual Methods. 2021;20:16094069211008333. doi: 10.1177/16094069211008333). However, further research is needed to determine their effectiveness and feasibility in different populations and settings. Tailored interventions that address the specific needs and preferences of patients with asthma and associated psychological factors are crucial for ensuring sustained and equitable use of these technologies. The manuscript emphasizes the importance of addressing psychological factors in the management and treatment of asthma and call for continued research and development in this area.
The paper aimed to provide a comprehensive overview of the use of digital health technologies in the assessment, treatment, and self-management of psychological and psychopathological factors associated with asthma. A collection of research articles and systematic reviews related to asthma, including topics such as outdoor air pollution, early life wheezing illnesses, atopic dermatitis, digital interventions for asthma self-management, psychiatric disorders and asthma, family influences on pediatric asthma, and the use of mobile health (mHealth) applications for asthma management, were analyzed. Eight selected studies were reviewed to assess the potential of digital health technologies in improving asthma psychological-related factors management and treatment outcomes. The reviewed studies suggest that electronic health (eHealth) interventions, mixed reality tools, mHealth technology-enhanced nurse-guided interventions, and smartphone applications integrating Bluetooth-enabled sensors for asthma inhalers can significantly improve symptom self-management, quality of life, and mental health outcomes, especially in children and adolescents with asthma (JMIR Pediatr Parent. 2019;2:e12427. doi: 10.2196/12427; Cochrane Database Syst Rev. 2018;8:CD012489. doi: 10.1002/14651858.CD012489.pub2; Int J Environ Res Public Health. 2020;17:7750. doi: 10.3390/ijerph17217750; J Med Internet Res. 2017;19:e113. doi: 10.2196/jmir.6994; J Med Internet Res. 2021;23:e25472. doi: 10.2196/25472; Ann Allergy Asthma Immunol. 2015;114:341–2.E2. doi: 10.1016/j.anai.2014.12.017; J Med Internet Res. 2022;24:e38030. doi: 10.2196/38030; Int J Qual Methods. 2021;20:16094069211008333. doi: 10.1177/16094069211008333). However, further research is needed to determine their effectiveness and feasibility in different populations and settings. Tailored interventions that address the specific needs and preferences of patients with asthma and associated psychological factors are crucial for ensuring sustained and equitable use of these technologies. The manuscript emphasizes the importance of addressing psychological factors in the management and treatment of asthma and call for continued research and development in this area.
DOI: https://doi.org/10.37349/edht.2024.00010
This article belongs to the special issue Telepsychiatry in Low-and Middle-income Countries: an Update
Twitter has been an invaluable social media platform for scientists to share research and host discourse among academics and the public. The change of ownership at Twitter has changed how scientists interact with the platform and has led some to worry about its future. This article discusses the current changes at Twitter and what implications these may have for future health research and communication.
Twitter has been an invaluable social media platform for scientists to share research and host discourse among academics and the public. The change of ownership at Twitter has changed how scientists interact with the platform and has led some to worry about its future. This article discusses the current changes at Twitter and what implications these may have for future health research and communication.
DOI: https://doi.org/10.37349/edht.2023.00002
This article belongs to the special issue Social Media Applications in Biomedical Research
Digital technologies have garnered more attention in this epoch of public health emergencies like coronavirus disease 2019 (COVID-19) and monkeypox (mpox). Digital twin (DT) is the virtual cybernetic equivalent of a physical object (e.g., a device, a human, a community) used to better understand the complexity of the latter and predict, prevent, monitor, and optimize real-world outcomes. The possible use cases of DT systems in public health ranging from mass vaccination planning to understanding disease transmission patterns have been discussed. Despite potential applications in healthcare, several economic, social, and ethical challenges might hinder the universal implementation of DT. Nevertheless, devising appropriate policies, reinforcing good governance, and launching multinational collaborative efforts ascertain early espousal of DT technology.
Digital technologies have garnered more attention in this epoch of public health emergencies like coronavirus disease 2019 (COVID-19) and monkeypox (mpox). Digital twin (DT) is the virtual cybernetic equivalent of a physical object (e.g., a device, a human, a community) used to better understand the complexity of the latter and predict, prevent, monitor, and optimize real-world outcomes. The possible use cases of DT systems in public health ranging from mass vaccination planning to understanding disease transmission patterns have been discussed. Despite potential applications in healthcare, several economic, social, and ethical challenges might hinder the universal implementation of DT. Nevertheless, devising appropriate policies, reinforcing good governance, and launching multinational collaborative efforts ascertain early espousal of DT technology.
DOI: https://doi.org/10.37349/edht.2023.00003
This article belongs to the special issue Social Media Applications in Biomedical Research
As part of the Advancing Science, Practice, Programming, and Policy in Research Translation for Children’s Environmental Health (ASP3IRE) center, machine learning, geographic information systems (GIS), and natural language processing to analyze more than 650 million posts related to children’s environmental health are being used. Using preliminary analyses as examples, this commentary discusses the potential opportunities, benefits, challenges, and limitations of children’s health social media analytics. Social media contains large volumes of contextually rich data that describe children’s health risks and needs, characteristics of homes and childcare locations important to environmental exposures, and parent and childcare provider perceptions, awareness of, and misconceptions about children’s environmental health. Twenty five million unique conversations mentioning children, with likes, views, and replies from more than 33 million X (formerly Twitter) users were identified. Many of these posts can be linked to traditional environmental and health data. However, social media analytics have several challenges and limitations. Challenges include a need for interdisciplinary collaborations, selectivity and sensitivity of analytical methods, the dynamic, evolving communication methods and platform preferences of social media users, and operational policies. Limitations include data availability, generalizability, and self-report bias. Social media analytics has significant potential to contribute to children’s environmental health research and translation.
As part of the Advancing Science, Practice, Programming, and Policy in Research Translation for Children’s Environmental Health (ASP3IRE) center, machine learning, geographic information systems (GIS), and natural language processing to analyze more than 650 million posts related to children’s environmental health are being used. Using preliminary analyses as examples, this commentary discusses the potential opportunities, benefits, challenges, and limitations of children’s health social media analytics. Social media contains large volumes of contextually rich data that describe children’s health risks and needs, characteristics of homes and childcare locations important to environmental exposures, and parent and childcare provider perceptions, awareness of, and misconceptions about children’s environmental health. Twenty five million unique conversations mentioning children, with likes, views, and replies from more than 33 million X (formerly Twitter) users were identified. Many of these posts can be linked to traditional environmental and health data. However, social media analytics have several challenges and limitations. Challenges include a need for interdisciplinary collaborations, selectivity and sensitivity of analytical methods, the dynamic, evolving communication methods and platform preferences of social media users, and operational policies. Limitations include data availability, generalizability, and self-report bias. Social media analytics has significant potential to contribute to children’s environmental health research and translation.
DOI: https://doi.org/10.37349/edht.2024.00011
This article belongs to the special issue Social Media Applications in Biomedical Research
Background:
The increase in powerful computers and technological devices as well as new forms of data analysis such as machine learning have resulted in the widespread availability of data science in healthcare. However, its role in organizations providing long-term care (LTC) for older people LTC for older adults has yet to be systematically synthesized. This analysis provides a state-of-the-art overview of 1) data science techniques that are used with data accumulated in LTC and for what specific purposes and, 2) the results of these techniques in researching the study objectives at hand.
Methods:
A scoping review based on guidelines of the Joanna Briggs Institute. PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched using keywords related to data science techniques and LTC. The screening and selection process was carried out by two authors and was not limited by any research design or publication date. A narrative synthesis was conducted based on the two aims.
Results:
The search strategy yielded 1,488 studies: 27 studies were included of which the majority were conducted in the US and in a nursing home setting. Text-mining/natural language processing (NLP) and support vector machines (SVMs) were the most deployed methods; accuracy was the most used metric. These techniques were primarily utilized for researching specific adverse outcomes including the identification of risk factors for falls and the prediction of frailty. All studies concluded that these techniques are valuable for their specific purposes.
Discussion:
This review reveals the limited use of data science techniques on data accumulated in or by LTC facilities. The low number of included articles in this review indicate the need for strategies aimed at the effective utilization of data with data science techniques and evidence of their practical benefits. There is a need for a wider adoption of these techniques in order to exploit data to their full potential and, consequently, improve the quality of care in LTC by making data-informed decisions.
Background:
The increase in powerful computers and technological devices as well as new forms of data analysis such as machine learning have resulted in the widespread availability of data science in healthcare. However, its role in organizations providing long-term care (LTC) for older people LTC for older adults has yet to be systematically synthesized. This analysis provides a state-of-the-art overview of 1) data science techniques that are used with data accumulated in LTC and for what specific purposes and, 2) the results of these techniques in researching the study objectives at hand.
Methods:
A scoping review based on guidelines of the Joanna Briggs Institute. PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched using keywords related to data science techniques and LTC. The screening and selection process was carried out by two authors and was not limited by any research design or publication date. A narrative synthesis was conducted based on the two aims.
Results:
The search strategy yielded 1,488 studies: 27 studies were included of which the majority were conducted in the US and in a nursing home setting. Text-mining/natural language processing (NLP) and support vector machines (SVMs) were the most deployed methods; accuracy was the most used metric. These techniques were primarily utilized for researching specific adverse outcomes including the identification of risk factors for falls and the prediction of frailty. All studies concluded that these techniques are valuable for their specific purposes.
Discussion:
This review reveals the limited use of data science techniques on data accumulated in or by LTC facilities. The low number of included articles in this review indicate the need for strategies aimed at the effective utilization of data with data science techniques and evidence of their practical benefits. There is a need for a wider adoption of these techniques in order to exploit data to their full potential and, consequently, improve the quality of care in LTC by making data-informed decisions.
DOI: https://doi.org/10.37349/edht.2024.00012
This article belongs to the special issue Data-informed Decision Making in Healthcare
Aim:
This study aimed to identify and analyze the top 100 most cited digital health and mobile health (m-health) publications. It could aid researchers in the identification of promising new research avenues, additionally supporting the establishment of international scientific collaboration between interdisciplinary research groups with demonstrated achievements in the area of interest.
Methods:
On 30th August, 2023, the Web of Science Core Collection (WOSCC) electronic database was queried to identify the top 100 most cited digital health papers with a comprehensive search string. From the initial search, 106 papers were identified. After screening for relevance, six papers were excluded, resulting in the final list of the top 100 papers. The basic bibliographic data was directly extracted from WOSCC using its “Analyze” and “Create Citation Report” functions. The complete records of the top 100 papers were downloaded and imported into a bibliometric software called VOSviewer (version 1.6.19) to generate an author keyword map and author collaboration map.
Results:
The top 100 papers on digital health received a total of 49,653 citations. Over half of them (n = 55) were published during 2013–2017. Among these 100 papers, 59 were original articles, 36 were reviews, 4 were editorial materials, and 1 was a proceeding paper. All papers were written in English. The University of London and the University of California system were the most represented affiliations. The USA and the UK were the most represented countries. The Journal of Medical Internet Research was the most represented journal. Several diseases and health conditions were identified as a focus of these works, including anxiety, depression, diabetes mellitus, cardiovascular diseases, and coronavirus disease 2019 (COVID-19).
Conclusions:
The findings underscore key areas of focus in the field and prominent contributors, providing a roadmap for future research in digital and m-health.
Aim:
This study aimed to identify and analyze the top 100 most cited digital health and mobile health (m-health) publications. It could aid researchers in the identification of promising new research avenues, additionally supporting the establishment of international scientific collaboration between interdisciplinary research groups with demonstrated achievements in the area of interest.
Methods:
On 30th August, 2023, the Web of Science Core Collection (WOSCC) electronic database was queried to identify the top 100 most cited digital health papers with a comprehensive search string. From the initial search, 106 papers were identified. After screening for relevance, six papers were excluded, resulting in the final list of the top 100 papers. The basic bibliographic data was directly extracted from WOSCC using its “Analyze” and “Create Citation Report” functions. The complete records of the top 100 papers were downloaded and imported into a bibliometric software called VOSviewer (version 1.6.19) to generate an author keyword map and author collaboration map.
Results:
The top 100 papers on digital health received a total of 49,653 citations. Over half of them (n = 55) were published during 2013–2017. Among these 100 papers, 59 were original articles, 36 were reviews, 4 were editorial materials, and 1 was a proceeding paper. All papers were written in English. The University of London and the University of California system were the most represented affiliations. The USA and the UK were the most represented countries. The Journal of Medical Internet Research was the most represented journal. Several diseases and health conditions were identified as a focus of these works, including anxiety, depression, diabetes mellitus, cardiovascular diseases, and coronavirus disease 2019 (COVID-19).
Conclusions:
The findings underscore key areas of focus in the field and prominent contributors, providing a roadmap for future research in digital and m-health.
DOI: https://doi.org/10.37349/edht.2024.00013
Artificial intelligence (AI) technology is advancing significantly, with many applications already in medicine, healthcare, and biomedical research. Among these fields, the area that AI is remarkably reshaping is biomedical scientific writing. Thousands of AI-based tools can be applied at every step of the writing process, improving time effectiveness, and streamlining authors’ workflow. Out of this variety, choosing the best software for a particular task may pose a challenge. While ChatGPT receives the necessary attention, other AI software should be addressed. In this review, we draw attention to a broad spectrum of AI tools to provide users with a perspective on which steps of their work can be improved. Several medical journals developed policies toward the usage of AI in writing. Even though they refer to the same technology, they differ, leaving a substantially gray area prone to abuse. To address this issue, we comprehensively discuss common ambiguities regarding AI in biomedical scientific writing, such as plagiarism, copyrights, and the obligation of reporting its implementation. In addition, this article aims to raise awareness about misconduct due to insufficient detection, lack of reporting, and unethical practices revolving around AI that might threaten unaware authors and medical society. We provide advice for authors who wish to implement AI in their daily work, emphasizing the need for transparency and the obligation together with the responsibility to maintain biomedical research credibility in the age of artificially enhanced science.
Artificial intelligence (AI) technology is advancing significantly, with many applications already in medicine, healthcare, and biomedical research. Among these fields, the area that AI is remarkably reshaping is biomedical scientific writing. Thousands of AI-based tools can be applied at every step of the writing process, improving time effectiveness, and streamlining authors’ workflow. Out of this variety, choosing the best software for a particular task may pose a challenge. While ChatGPT receives the necessary attention, other AI software should be addressed. In this review, we draw attention to a broad spectrum of AI tools to provide users with a perspective on which steps of their work can be improved. Several medical journals developed policies toward the usage of AI in writing. Even though they refer to the same technology, they differ, leaving a substantially gray area prone to abuse. To address this issue, we comprehensively discuss common ambiguities regarding AI in biomedical scientific writing, such as plagiarism, copyrights, and the obligation of reporting its implementation. In addition, this article aims to raise awareness about misconduct due to insufficient detection, lack of reporting, and unethical practices revolving around AI that might threaten unaware authors and medical society. We provide advice for authors who wish to implement AI in their daily work, emphasizing the need for transparency and the obligation together with the responsibility to maintain biomedical research credibility in the age of artificially enhanced science.
DOI: https://doi.org/10.37349/edht.2024.00024
Photographic images are an essential tool in oral medicine practice, even though their value is conditioned by their quality. Digital photography using smartphones (SPhs) has had many advances, nowadays allowing the acquisition of high-quality pictures. Compared to professional cameras, it has advantages and disadvantages. The latter comprise photographs out of focus, poorly framed, and lighting problems due to shadows, artifacts, and color alterations, among other problems mainly mediated by the operator. Such defects can limit the proper interpretation of the image representing the patient’s condition. This perspective aims to describe the basic concepts of photography and the functional features of SPhs. This will allow programming those devices properly for oral telemedicine (OTM), understanding their limitations, and correcting errors for the photographs to be used effectively. We also include empirical solutions and illustrations showing that photography with SPhs could be easily executable by any health professional and even by the patients themselves.
Photographic images are an essential tool in oral medicine practice, even though their value is conditioned by their quality. Digital photography using smartphones (SPhs) has had many advances, nowadays allowing the acquisition of high-quality pictures. Compared to professional cameras, it has advantages and disadvantages. The latter comprise photographs out of focus, poorly framed, and lighting problems due to shadows, artifacts, and color alterations, among other problems mainly mediated by the operator. Such defects can limit the proper interpretation of the image representing the patient’s condition. This perspective aims to describe the basic concepts of photography and the functional features of SPhs. This will allow programming those devices properly for oral telemedicine (OTM), understanding their limitations, and correcting errors for the photographs to be used effectively. We also include empirical solutions and illustrations showing that photography with SPhs could be easily executable by any health professional and even by the patients themselves.
DOI: https://doi.org/10.37349/edht.2024.00025
This article belongs to the special issue Digital Health Technologies for the Early Detection of Oral Cancer
ChatGPT is one of the promising AI-based language models which has the potential to contribute to pharmacy settings in many aspects. This paper focuses on the possible aspects of pharmacy management where ChatGPT can contribute, the prevalence of its use in Saudi Arabia as a practical insight, case studies showing the potential of ChatGPT in answering health-related enquiries, its benefits, challenges, and future prospects of it. Helping clients, verifying medication, examining for potential reactions to drugs, identifying potential interaction between drugs, providing recommendation for suitable alternative medication therapies, assisting healthcare workers and supporting the search for novel medication are the biggest roles that are cited. The study highlights several benefits of using ChatGPT, including greater medical supervision, fewer drug errors, greater power over existing equipment, and support to study about the medicine sector. However, concerns about security, reliability, privacy, over-reliance on AI, and lack of natural judgement must be addressed by careful implementation under human review. The study also provided insight of practical application of ChatGPT in pharmacy education and possible ways of implementing ChatGPT in getting improved care and optimized operation. The future prospect of ChatGPT is promising but requires increased precision, integration of it into education programs, progressing of patient treatment and interaction, and facilitating novel research abilities. In general, the review suggests that ChatGPT has the potential to improve and modernize pharmacy processes but cautious implementation of this developing AI technology, combined with human knowledge is important to improve healthcare in the pharmaceutical field.
ChatGPT is one of the promising AI-based language models which has the potential to contribute to pharmacy settings in many aspects. This paper focuses on the possible aspects of pharmacy management where ChatGPT can contribute, the prevalence of its use in Saudi Arabia as a practical insight, case studies showing the potential of ChatGPT in answering health-related enquiries, its benefits, challenges, and future prospects of it. Helping clients, verifying medication, examining for potential reactions to drugs, identifying potential interaction between drugs, providing recommendation for suitable alternative medication therapies, assisting healthcare workers and supporting the search for novel medication are the biggest roles that are cited. The study highlights several benefits of using ChatGPT, including greater medical supervision, fewer drug errors, greater power over existing equipment, and support to study about the medicine sector. However, concerns about security, reliability, privacy, over-reliance on AI, and lack of natural judgement must be addressed by careful implementation under human review. The study also provided insight of practical application of ChatGPT in pharmacy education and possible ways of implementing ChatGPT in getting improved care and optimized operation. The future prospect of ChatGPT is promising but requires increased precision, integration of it into education programs, progressing of patient treatment and interaction, and facilitating novel research abilities. In general, the review suggests that ChatGPT has the potential to improve and modernize pharmacy processes but cautious implementation of this developing AI technology, combined with human knowledge is important to improve healthcare in the pharmaceutical field.
DOI: https://doi.org/10.37349/edht.2024.00026
In the United Kingdom (UK), the current prevalence rates of oropharyngeal cancer linked to human papillomavirus (HPV) are 6.29% and 2.04% in men and women, respectively. Over the years, the burden of this disease has increased in the UK, and this is mainly due to the rising prevalence of HPV infection in the UK. Research evidence has shown that over 70% of oropharyngeal cancers in the UK are linked to HPV. Oral sex is the major route of transmission of HPV, and over 63% of UK young adults are found to have a positive history of oral sex practice. However, only a minority of the UK population are aware of HPV-associated oropharyngeal cancer; this therefore calls for more public health efforts to increase awareness and knowledge on HPV-associated oropharyngeal cancer in the UK. While the use of technology-based, clinic-based, and community-based interventions have been employed to improve public awareness and knowledge on the role of HPV-associated oropharyngeal cancer, mobile health (mhealth) interventions have not been seriously explored despite existing robust evidence on the effective roles of mhealth in improving awareness and knowledge in diverse diseases. This article therefore calls for the adoption and use of mhealth interventions in educating the UK’s population on HPV-associated oropharyngeal cancer. The use of mhealth interventions in this regard is highly viable as its implementation closely aligns with the country’s National Health Service (NHS) commitment towards the digital transformation of the UK’s healthcare system.
In the United Kingdom (UK), the current prevalence rates of oropharyngeal cancer linked to human papillomavirus (HPV) are 6.29% and 2.04% in men and women, respectively. Over the years, the burden of this disease has increased in the UK, and this is mainly due to the rising prevalence of HPV infection in the UK. Research evidence has shown that over 70% of oropharyngeal cancers in the UK are linked to HPV. Oral sex is the major route of transmission of HPV, and over 63% of UK young adults are found to have a positive history of oral sex practice. However, only a minority of the UK population are aware of HPV-associated oropharyngeal cancer; this therefore calls for more public health efforts to increase awareness and knowledge on HPV-associated oropharyngeal cancer in the UK. While the use of technology-based, clinic-based, and community-based interventions have been employed to improve public awareness and knowledge on the role of HPV-associated oropharyngeal cancer, mobile health (mhealth) interventions have not been seriously explored despite existing robust evidence on the effective roles of mhealth in improving awareness and knowledge in diverse diseases. This article therefore calls for the adoption and use of mhealth interventions in educating the UK’s population on HPV-associated oropharyngeal cancer. The use of mhealth interventions in this regard is highly viable as its implementation closely aligns with the country’s National Health Service (NHS) commitment towards the digital transformation of the UK’s healthcare system.
DOI: https://doi.org/10.37349/edht.2024.00027
This article belongs to the special issue Digital Health Technologies for the Early Detection of Oral Cancer
Geographic areas like Argentine Patagonia face significant barriers in the fight against oral cancer due to great distances, extreme weather conditions, and a shortage of specialists. These factors contribute to delayed diagnosis and treatment, adversely affecting patient outcomes. The aim of this study was to describe a pilot project to establish the telemedicine network of Chubut (Argentine Patagonia) for the early diagnosis of oral cancer. This perspective study also aimed to describe the advantages and disadvantages of using this tool in remote areas with limited access to healthcare services. Healthcare professionals, including nurses, dentists, doctors, and healthcare workers, were trained in the early diagnosis of oral cancer and high-risk oral lesions by five specialists in Oral Medicine, who traveled throughout Argentine Patagonia. Additionally, training was provided on the use of smartphones to obtain clinical images and data for remote consultations via telemedicine with a specialized center. Over 2,000 km were traveled, and more than 100 healthcare professionals were trained in six towns and localities in Patagonia, Argentina, encountering various limitations for the use of telemedicine in remote areas, such as connectivity issues. The first telemedicine network of Patagonia for the diagnosis of oral cancer was created and is now operational, receiving teleconsultations and referrals from the professionals trained during the journey. This study highlighted that telemedicine is an important tool to overcome geographical barriers and improve access to medical care, especially in remote areas. It promotes agility and speed in referrals and optimizes the available resources of the health system. Future studies should analyze the impact of telemedicine in decreasing the delay of oral cancer diagnosis in Southern Argentina.
Geographic areas like Argentine Patagonia face significant barriers in the fight against oral cancer due to great distances, extreme weather conditions, and a shortage of specialists. These factors contribute to delayed diagnosis and treatment, adversely affecting patient outcomes. The aim of this study was to describe a pilot project to establish the telemedicine network of Chubut (Argentine Patagonia) for the early diagnosis of oral cancer. This perspective study also aimed to describe the advantages and disadvantages of using this tool in remote areas with limited access to healthcare services. Healthcare professionals, including nurses, dentists, doctors, and healthcare workers, were trained in the early diagnosis of oral cancer and high-risk oral lesions by five specialists in Oral Medicine, who traveled throughout Argentine Patagonia. Additionally, training was provided on the use of smartphones to obtain clinical images and data for remote consultations via telemedicine with a specialized center. Over 2,000 km were traveled, and more than 100 healthcare professionals were trained in six towns and localities in Patagonia, Argentina, encountering various limitations for the use of telemedicine in remote areas, such as connectivity issues. The first telemedicine network of Patagonia for the diagnosis of oral cancer was created and is now operational, receiving teleconsultations and referrals from the professionals trained during the journey. This study highlighted that telemedicine is an important tool to overcome geographical barriers and improve access to medical care, especially in remote areas. It promotes agility and speed in referrals and optimizes the available resources of the health system. Future studies should analyze the impact of telemedicine in decreasing the delay of oral cancer diagnosis in Southern Argentina.
DOI: https://doi.org/10.37349/edht.2024.00028
This article belongs to the special issue Digital Health Technologies for the Early Detection of Oral Cancer
Teledentistry has emerged as a promising tool in bridging the gap in healthcare accessibility, particularly in regions like Latin America region, where resources for oral healthcare are often limited. Drawing upon a comprehensive review of literature, this overview assessed the applications and clinical outcomes of teledentistry in diagnosing oral potentially malignant disorders (OPMDs) and oral cancer, highlighting the challenges and opportunities specific to the Latin American context. Moreover, it examined the integration of artificial intelligence algorithms and teledentistry for enhancing diagnostic accuracy, thereby optimizing resource allocation and improving patient outcomes. By elucidating the current landscape and future prospects, this overview provided insights for policymakers, healthcare providers, and researchers, fostering advancements in oral healthcare delivery with the aim of reducing the burden of OPMDs and oral cancer in the Latin American region.
Teledentistry has emerged as a promising tool in bridging the gap in healthcare accessibility, particularly in regions like Latin America region, where resources for oral healthcare are often limited. Drawing upon a comprehensive review of literature, this overview assessed the applications and clinical outcomes of teledentistry in diagnosing oral potentially malignant disorders (OPMDs) and oral cancer, highlighting the challenges and opportunities specific to the Latin American context. Moreover, it examined the integration of artificial intelligence algorithms and teledentistry for enhancing diagnostic accuracy, thereby optimizing resource allocation and improving patient outcomes. By elucidating the current landscape and future prospects, this overview provided insights for policymakers, healthcare providers, and researchers, fostering advancements in oral healthcare delivery with the aim of reducing the burden of OPMDs and oral cancer in the Latin American region.
DOI: https://doi.org/10.37349/edht.2024.00029
This article belongs to the special issue Digital Health Technologies for the Early Detection of Oral Cancer
Background:
This study addresses the complexities of utilizing blockchain technology in healthcare, aiming to provide a decision-making tool for healthcare professionals and policymakers evaluating blockchain’s suitability for healthcare data sharing applications.
Methods:
A tertiary review was conducted on existing systematic literature reviews concerning blockchain in the healthcare domain. Reviews that focused on data sharing were selected, and common key factors assessing blockchain’s suitability in healthcare were extracted.
Results:
Our review synthesized findings from 27 systematic literature reviews, which led to the development of a refined decision-making flowchart. This tool outlines criteria such as scalability, integrity/immutability, interoperability, transparency, patient involvement, cost, and public verifiability, essential for assessing the suitability of blockchain in healthcare data sharing. This flowchart was validated through multiple case studies from various healthcare domains, testing its utility in real-world scenarios.
Discussion:
Blockchain technology could significantly benefit healthcare data sharing, provided its application is carefully evaluated against tailored criteria for healthcare needs. The decision-making flowchart developed from this review offers a systematic approach to assist stakeholders in navigating the complexities of implementing blockchain technology in healthcare settings.
Background:
This study addresses the complexities of utilizing blockchain technology in healthcare, aiming to provide a decision-making tool for healthcare professionals and policymakers evaluating blockchain’s suitability for healthcare data sharing applications.
Methods:
A tertiary review was conducted on existing systematic literature reviews concerning blockchain in the healthcare domain. Reviews that focused on data sharing were selected, and common key factors assessing blockchain’s suitability in healthcare were extracted.
Results:
Our review synthesized findings from 27 systematic literature reviews, which led to the development of a refined decision-making flowchart. This tool outlines criteria such as scalability, integrity/immutability, interoperability, transparency, patient involvement, cost, and public verifiability, essential for assessing the suitability of blockchain in healthcare data sharing. This flowchart was validated through multiple case studies from various healthcare domains, testing its utility in real-world scenarios.
Discussion:
Blockchain technology could significantly benefit healthcare data sharing, provided its application is carefully evaluated against tailored criteria for healthcare needs. The decision-making flowchart developed from this review offers a systematic approach to assist stakeholders in navigating the complexities of implementing blockchain technology in healthcare settings.
DOI: https://doi.org/10.37349/edht.2024.00014
This article belongs to the special issue Data-informed Decision Making in Healthcare
