Transformer Models in Healthcare: A Survey and Thematic Analysis of Potentials, Shortcomings and Risks
This study, conducted with 28 participants using a qualitative approach, explores the benefits, shortcomings, and risks of using transformer models in healthcare. It analyses responses to seven open-ended questions using a simplified thematic analysis. Our research reveals seven benefits, including improved operational efficiency, optimized processes and refined clinical documentation. Despite these benefits, there are significant concerns about the introduction of bias, auditability issues and privacy risks. Challenges include the need for specialized expertise, the emergence of ethical dilemmas and the potential reductio...
Source: Journal of Medical Systems - February 17, 2024 Category: Information Technology Source Type: research
The Breakthrough of Large Language Models Release for Medical Applications: 1-Year Timeline and Perspectives
AbstractWithin the domain of Natural Language Processing (NLP), Large Language Models (LLMs) represent sophisticated models engineered to comprehend, generate, and manipulate text resembling human language on an extensive scale. They are transformer-based deep learning architectures, obtained through the scaling of model size, pretraining of corpora, and computational resources. The potential healthcare applications of these models primarily involve chatbots and interaction systems for clinical documentation management, and medical literature summarization (Biomedical NLP). The challenge in this field lies in the research ...
Source: Journal of Medical Systems - February 17, 2024 Category: Information Technology Source Type: research
Transformer Models in Healthcare: A Survey and Thematic Analysis of Potentials, Shortcomings and Risks
This study, conducted with 28 participants using a qualitative approach, explores the benefits, shortcomings, and risks of using transformer models in healthcare. It analyses responses to seven open-ended questions using a simplified thematic analysis. Our research reveals seven benefits, including improved operational efficiency, optimized processes and refined clinical documentation. Despite these benefits, there are significant concerns about the introduction of bias, auditability issues and privacy risks. Challenges include the need for specialized expertise, the emergence of ethical dilemmas and the potential reductio...
Source: Journal of Medical Systems - February 17, 2024 Category: Information Technology Source Type: research
The Breakthrough of Large Language Models Release for Medical Applications: 1-Year Timeline and Perspectives
AbstractWithin the domain of Natural Language Processing (NLP), Large Language Models (LLMs) represent sophisticated models engineered to comprehend, generate, and manipulate text resembling human language on an extensive scale. They are transformer-based deep learning architectures, obtained through the scaling of model size, pretraining of corpora, and computational resources. The potential healthcare applications of these models primarily involve chatbots and interaction systems for clinical documentation management, and medical literature summarization (Biomedical NLP). The challenge in this field lies in the research ...
Source: Journal of Medical Systems - February 17, 2024 Category: Information Technology Source Type: research
Text-Mining and Video Analytics of COVID-19 Narratives Shared by Patients on YouTube
This study explores how individuals who have experienced COVID-19 share their stories on YouTube, focusing on the nature of information disclosure, public engagement, and emotional impact pertaining to consumer health. Using a dataset of 186 YouTube videos, we used text mining and video analytics techniques to analyze textual transcripts and visual frames to identify themes, emotions, and their relationship with viewer engagement metrics. Findings reveal eight key themes: infection origins, symptoms, treatment, mental well-being, isolation, prevention, government directives, and vaccination. While viewers engaged most with...
Source: Journal of Medical Systems - February 15, 2024 Category: Information Technology Source Type: research
Artificial Intelligence in Operating Room Management
AbstractThis systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected studies from February 2019 to September 2023 are analyzed. The review emphasizes the significant impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as ...
Source: Journal of Medical Systems - February 14, 2024 Category: Information Technology Source Type: research
An mHealth Application in German Health Care System: Importance of User Participation in the Development Process
AbstractThis paper addresses the challenges and solutions in developing a holistic prevention mobile health application (mHealth app) for Germany ’s healthcare sector. Despite Germany’s lag in healthcare digitalization, the app aims to enhance primary prevention in physical activity, nutrition, and stress management. A significant focus is on user participation and usability to counter the prevalent issue of user attrition in mHealth appl ications, as described by Eysenbach’s ‘law of attrition’. The development process, conducted in a scientific and university context, faces constraints like limited budgets and e...
Source: Journal of Medical Systems - February 14, 2024 Category: Information Technology Source Type: research
Artificial Intelligence in Operating Room Management
AbstractThis systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected studies from February 2019 to September 2023 are analyzed. The review emphasizes the significant impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as ...
Source: Journal of Medical Systems - February 14, 2024 Category: Information Technology Source Type: research
An mHealth Application in German Health Care System: Importance of User Participation in the Development Process
AbstractThis paper addresses the challenges and solutions in developing a holistic prevention mobile health application (mHealth app) for Germany ’s healthcare sector. Despite Germany’s lag in healthcare digitalization, the app aims to enhance primary prevention in physical activity, nutrition, and stress management. A significant focus is on user participation and usability to counter the prevalent issue of user attrition in mHealth appl ications, as described by Eysenbach’s ‘law of attrition’. The development process, conducted in a scientific and university context, faces constraints like limited budgets and e...
Source: Journal of Medical Systems - February 14, 2024 Category: Information Technology Source Type: research
Measuring the Coverage of the HL7 ® FHIR® Standard in Supporting Data Acquisition for 3 Public Health Registries
AbstractWith the increasing need for timely submission of data to state and national public health registries, current manual approaches to data acquisition and submission are insufficient. In clinical practice, federal regulations are now mandating the use of data messaging standards, i.e., the Health Level Seven (HL7®) Fast Healthcare Interoperability Resources (FHIR®) standard, to facilitate the electronic exchange of clinical (patient) data. In both research and public health practice, we can also leverage FHIR®‒ and the infrastructure already in place for supporting exchange of clinical practice data ‒ to enabl...
Source: Journal of Medical Systems - February 8, 2024 Category: Information Technology Source Type: research
ChatGPT for Parents of Children Seeking Emergency Care – so much Hope, so much Caution
(Source: Journal of Medical Systems)
Source: Journal of Medical Systems - February 2, 2024 Category: Information Technology Source Type: research
Adoption and Sustained Use of Primary Care Video Visits Among Veterans with VA Video-Enabled Tablets
We examined patient characteristics associated with adoption and sustained use of video-based primary care among Veterans. We conducted a retrospective cohort study of Veterans who received VA-issued tablets between 3/11/2020-9/10/2020. We used generalized linear models to evaluate the sociodemographic and clinical factors associated with video-based primary care adoption (i.e., likelihood of having a primary care video visit) and sustained use (i.e., rate of video care) in the six months after a Veteran received a VA-issued tablet. Of the 36,077 Veterans who received a tablet, 69% had at least one video-based visit within...
Source: Journal of Medical Systems - January 30, 2024 Category: Information Technology Source Type: research
