Objective: The complexity of the Fast Healthcare Interoperability Resources (FHIR) standard limits its direct usability for clinicians despite its transformative potential in healthcare data management. To bridge this gap, we aimed to describe the development of an interactive dashboard enabling non-technical users to intuitively build and analyze #Oncologic #Patient cohorts. By leveraging FHIR, we aimed to enhance data accessibility and interoperability in clinical practice. Methods: DermaDashboard builds on a Structured Query Language (SQL) database using a relational FHIR model, which ensures data compliance with the FHIR schema. A materialized view was assembled and optimized performance by providing only relevant data. The user interface was built with Grafana and supports intuitive data exploration. We applied DermaDashboard to the use case of melanoma, demonstrating its utility in real-world #Oncologic cohort analyses. Results: DermaDashboard was successfully built and integrated into the clinical environment, identifying 3,949 melanoma #Patients and corresponding to 82,783 electronic health records. The primary FHIR resources used were #Patient, DiagnosticReport, and QuestionnaireResponse, and captured 54 data attributes, including demographics, histological classifications, genetic mutations, clinical and pathological staging, treatments, and procedures. Clinicians can filter the data using 29 variables to create specific subcohorts. The dashboard also enables operational insights by tracking annual trends in procedures and drug administrations. Conclusions: DermaDashboard enhances data accessibility for non-technical clinical users while showcasing the power of FHIR standardization in healthcare applications. By enabling #Oncological insights and identifying cohort discrepancies, it enhances both clinical decision-making and data quality.

The programme aims to provide networking opportunities and career development to enhance the future careers of successful postdoc fellows. Purpose Digital Futures postdoc fellowships aim to support ta...

Background: Emergency department (ED) overcrowding remains a critical challenge, leading to delays in #patient care and increased operational strain. Current hospital management strategies often rely on reactive decision-making, addressing congestion only after it occurs. However, effective #patient flow management requires early identification of overcrowding risks to allow timely interventions. Machine learning (ML)–based predictive modeling offers a solution by forecasting key #patient flow measures, such as waiting count, enabling proactive resource allocation and improved hospital efficiency. Objective: The aim of this study is to develop ML models that predict ED waiting room occupancy (waiting count) at 2 temporal resolutions. The first approach is the hourly prediction model, which estimates the waiting count exactly 6 hours ahead at each prediction time (eg, a 1 PM prediction forecasts 7 PM). The second approach is the daily prediction model, which forecasts the average waiting count for the next 24-hour period (eg, a 5 PM prediction estimates the following day’s average). These predictive tools support resource allocation and help mitigate overcrowding by enabling proactive interventions before congestion occurs. Methods: Data from a partner hospital’s ED in the southeastern United States were used, integrating internal and external sources. Eleven different ML algorithms, ranging from traditional approaches to deep learning architectures, were systematically trained and evaluated on both hourly and daily predictions to determine the models that achieved the lowest prediction error. Experiments optimized feature combinations, and the best models were tested under high #patient volume and across different hours to assess temporal accuracy. Results: The best hourly prediction performance was achieved by time series vision transformer plus (TSiTPlus) with a mean absolute error (MAE) of 4.19 and a mean squared error (MSE) of 29.36. The overall hourly waiting count had a mean of 18.11 and a SD (σ) of 9.77. Prediction accuracy varied by time of day, with the lowest MAE at 11 PM (2.45) and the highest at 8 PM (5.45). Extreme case analysis at (mean + 1σ), (mean + 2σ), and (mean + 3σ) resulted in MAEs of 6.16, 10.16, and 15.59, respectively. For daily predictions, an explainable convolutional neural network plus (XCMPlus) achieved the best results with an MAE of 2.00 and a MSE of 6.64. The daily waiting count had a mean of 18.11 and a SD of 4.51. Both models outperformed traditional forecasting approaches across multiple evaluation metrics. Conclusions: The proposed prediction models effectively forecast ED waiting count at both hourly and daily intervals. The results demonstrate the value of integrating diverse data sources and applying advanced modeling techniques to support proactive resource allocation decisions. The implementation of these forecasting tools within hospital management systems has the potential to improve #patient flow and reduce overcrowding in emergency care settings. The code is available in our GitHub repository. Trial Registration:

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Intraoperative hypotension (IOH) is associated with an increased risk of heart and kidney complications. Although AI tools aim to predict IOH, their real-world reliability is often overstated due to b...

Researchers reveal website keystroke tracking that captures what users type, even without form submission, raising privacy concerns.

Smart hospitals utilize advanced technology including AI and automation to improve patient care and streamline workflow.

If you’re an athlete who does a mix of low- and high-intensity intervals, your heart rate monitor’s accuracy can suffer. For the most precise tracking of intervals, consider using the RR interval data (which all monitors supply) instead and syncing your device post-workout.

Background: Systematic reviews are essential for synthesising research in health sciences, yet they are resource-intensive and prone to human error. The data extraction phase, where key details of studies are identified and recorded in a systematic manner, may benefit from the application of automation processes. Recent advancements in artificial intelligence (#AI) (AI), specifically Large Language Models (LLMs) like ChatGPT, may streamline this process. Objective: This study aims to develop and evaluate a custom Generative Pre-Training Transformer (GPT), named Systematic Review Extractor Pro, for automating the data extraction phase of systematic reviews in health research Methods: OpenAI's GPT Builder was used to create a GPT tailored to extract information from academic manuscripts. The Role, Instruction, Steps, End goal, and Narrowing (RISEN) framework was used to inform prompt engineering for the GPT. A sample of 20 studies across two distinct systematic reviews was used to evaluate the GPT's performance in extraction. Agreement rates between the GPT outputs and human reviewers were calculated for each study subsection. Results: Mean time for human extraction was 36 minutes per study, compared to 26.6 seconds for the GPT plus 13 minutes of human review. The GPT demonstrated high overall agreement rates with human reviewers, achieving 91.45% for review 1 and 89.31% for review 2. It was particularly accurate in extracting study (review 1: 95.25; review 2: 90.83%) and participant (review 1: 95.03%; review 2: 90.00%) characteristics, with lower performance observed in more complex areas such as methodological characteristics (87.07%) and statistical results (77.50%). The GPT correct when the human reviewer was incorrect in 14 instances (3.25%) in review 1 and four instances (1.16% in review 2). Conclusions: The custom GPT significantly reduced extraction time and shows evidence that it can extract data with high accuracy, particularly participant and study characteristics. It was most effective in extracting information such as study and participant characteristics. This tool may offer a viable option for researchers seeking to reduce resource demands during the extraction phase, though more research is needed to evaluate test-retest reliability, performance across broader review types, and accuracy extracting statistical data. The tool in the current study has been made open access.

Muse S Athena headband combines EEG and fNIRS for brain monitoring at home. Dive into the world of portable neurotech and its potential for sleep science.

Background: Obesity has become one of today's global health challenges. According to the World Health Organisation, in 2022, 2.5 billion adults aged 18 years and older will be overweight, including more than 890 million adults with obesity. Objective: Exercise interventions based on mobile health technology are widely available, but the effectiveness and feasibility of interventions using mobile health apps and exercise watches to improve the physical health of overweight and obese male college students are unknown, and this study compares the effects of online interventions carried out by mobile health technology and offline interventions guided by physical trainers on the physical health of overweight and obese male college students. Methods: This study used a randomised controlled trial with a pre-test post-test design, and participants were randomly divided into an online group, an offline group and a control group. The online group exercised online through the fitness APP, and the offline group was instructed by a professional trainer to exercise offline, and both groups wore sports watches to monitor their activities, and the training content was the same. The control group did not carry out any intervention. Results: At the end of the intervention, the BMI of the online and offline groups decreased by 1.5 kg/m² and 1.6 kg/m², respectively (P

The appearance of thousands of formulaic biomedical studies has been linked to the rise of text-generating AI tools.

Background The National Early Warning Score (NEWS) is implemented internationally for in-hospital monitoring. It has been superior to other predictive scores, but its preventive abilities are still…