Objective: During surgery it is common to measure the arterial blood pressure. One important reason is to monitor for hypotension, a too low blood pressure, which is known to be harmful. Since post-su...

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A device you attach to your underwear reveals how often you really break wind – and it’s probably more frequently than you think

The incompleteness theorem is accepted as part of the mathematical canon today, but columnist Jacob Aron says it was a bombshell when Kurt Gödel first introduced it. Gödel’s seminal work directly contradicted one of the great minds of mathematics and limited the field forever

Canonical has opened up Resolute Raccoon for testing, and the beta shows promise.

Background: #Mobile #Health (#mHealth) apps are widely available, and some have proven safe and effective for management of specific chronic conditions. Despite a high degree of interest, the potential of these technologies has yet to be realized. Patient and clinician attitudes are key factors that influence the adoption of #mHealth apps but remain poorly understood, particularly in the United States. Objective: This study aimed to identify both patient and clinician attitudes that can influence recommending and adopting #mHealth apps. Methods: Using well-established technology adoption and implementation science frameworks, this study included a deductive content analysis using a rapid qualitative analytic method. Semistructured interviews were conducted with patients and clinicians to identify technical and material, social and personal, and policy and organizational factors that can influence the recommendation or adoption of #mHealth apps. The interviews and data analysis were performed between September 2023 and August 2024. Results: Participants included 20 clinicians (n=12, 60% general internists) with a mean time in practice of 17 (SD 11.6) years, and 28 patients with a mean age of 59 (SD 12.1) years. A total of 7 categories related to patients’ and clinicians’ attitudes toward #mHealth apps emerged: (1) apps as tools to improve #Health by extending care, (2) the role of apps in enhancing the patient–clinician relationship, (3) the need for simplicity and efficiency in #App design, (4) the influence of prior experience with #mHealth apps, (5) comfort with technology, (6) recommendations from trusted sources, and (7) education and hands-on experience. Although similar factors were considered by patients and clinicians, their views about older adults’ interest and ability to use #mHealth apps differed. Conclusions: Understanding patient and clinician views about #mHealth apps provides critical insights for developing approaches to facilitate their use. These findings suggest patients and clinicians share similar views about the benefits of #mHealth apps. Nonetheless, clinicians’ perceptions about older patients’ interest and ability to use #mHealth apps may negatively impact recommendation of #mHealth apps and subsequent adoption by older adults.

Subject field Medical imaging with specialization in imaging technologies in vivo. Subject description Medical imaging with specialization in imaging technologies in vivo refers to imaging of the livi

An engineer built a flying umbrella that uses computer vision to track your every move and hover overhead, hands-free, in the rain.

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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