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…

Journal of Clinical Monitoring and Computing - Purpose: Weaning from mechanical ventilation (MV) is the transition from ventilator dependence to independent breathing. Optimal timing reduces...

The UnitedHealth-owned medical coding service was hacked earlier this year by a ransomware gang.

Background: Large language models (LLMs) can generate outputs understandable by humans, such as answers to medical questions and radiology reports. With the rapid development of LLMs, clinicians face a growing challenge in determining the most suitable algorithms to support their work. Objective: We aimed to provide clinicians and other health care practitioners with systematic guidance in selecting an LLM that is relevant and appropriate to their needs and facilitate the integration process of LLMs in health care. Methods: We conducted a literature search of full-text publications in English on clinical applications of LLMs published between January 1, 2022, and March 31, 2025, on PubMed, ScienceDirect, Scopus, and IEEE Xplore. We excluded papers from journals below a set citation threshold, as well as papers that did not focus on LLMs, were not research based, or did not involve clinical applications. We also conducted a literature search on arXiv within the same investigated period and included papers on the clinical applications of innovative multimodal LLMs. This led to a total of 270 studies. Results: We collected 330 LLMs and recorded their application frequency in clinical tasks and frequency of best performance in their context. On the basis of a 5-stage clinical workflow, we found that stages 2, 3, and 4 are key stages in the clinical workflow, involving numerous clinical subtasks and LLMs. However, the diversity of LLMs that may perform optimally in each context remains limited. GPT-3.5 and GPT-4 were the most versatile models in the 5-stage clinical workflow, applied to 52% (29/56) and 71% (40/56) of the clinical subtasks, respectively, and they performed best in 29% (16/56) and 54% (30/56) of the clinical subtasks, respectively. General-purpose LLMs may not perform well in specialized areas as they often require lightweight prompt engineering methods or fine-tuning techniques based on specific datasets to improve model performance. Most LLMs with multimodal abilities are closed-source models and, therefore, lack of transparency, model customization, and fine-tuning for specific clinical tasks and may also pose challenges regarding data protection and privacy, which are common requirements in clinical settings. Conclusions: In this review, we found that LLMs may help clinicians in a variety of clinical tasks. However, we did not find evidence of generalist clinical LLMs successfully applicable to a wide range of clinical tasks. Therefore, their clinical deployment remains challenging. On the basis of this review, we propose an interactive online guideline for clinicians to select suitable LLMs by clinical task. With a clinical perspective and free of unnecessary technical jargon, this guideline may be used as a reference to successfully apply LLMs in clinical settings.

Dynamic response of Bluetooth wearable heart rate monitors during rapid changes in heart rate, Sabioni, Mariah, Willén, Jonas, Dual, Seraina Anne, Jacobsson, Martin

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