RAVCARE — a telehealth platform designed specifically for clinical use — offers a suite of features that help healthcare organizations optimise clinical workflows, improve patient experience, increase...

As machine learning and AI-enabled tools increasingly compete for limited visual workspace, the need for standardized monitor configurations has become more urgent. Establishing shared standards can reduce inefficiencies, minimize workstation adjustment time, and support clinicians who regularly rotate across multiple reading environments—particularly in academic settings where practitioners may work in…

Daniel Brearley, clinical product and applications specialist at Canon Medical Systems UK, explains why interventional radiology must move the system, not the patient.

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d329759e58">Technological advances in radiation oncology that promise improvements in workflow efficie...

New Plug-In BLE Sensors provide a faster, more cost-effective path to facility-wide RTLS coverage with near-room location precision. VERSAILLES, Ohio, Jan. 30, 2026 /PRNewswire/ — Midmark RTLS, a leading real-time locating system (RTLS) technology provider focused on clinical workflow solutions and data insights that improve the delivery of care, today announced the official availability of the Midmark RTLS Bluetooth Low Energy (BLE) Sensory Network, a scalable, easy-to-deploy solution designed to help health systems rapidly achieve near-room location precision for staff, patients and equipment. This simplified approach delivers both facility-wide coverage and a future-ready foundation that can scale with operational and […]

Background: The global demand for assistive devices, such as prosthetics and orthotics, is increasing. A shortage of trained professionals contributes to suboptimal care. To improve clinical workflows, the Life Lounge Clinical Workflow (LLCW) has been developed. Understanding user acceptance is essential for ensuring its successful implementation. Objective: This study explored prosthetists and orthotists professionals’ perceptions and acceptance of LLCW, as well as the perceived benefits and challenges associated with its use. Methods: A postdemonstration mixed methods study was conducted using the unified theory of acceptance and use of technology framework, combining Likert-scale summaries with thematic analysis of open-text responses. The study included 18 prosthetists and orthotists professionals working at orthotic and prosthetic clinics across Sweden. After an interactive session about LLCW, feedback was collected via questionnaires. Thematic analysis was used to analyze the data. Results: Participants rated several acceptance-related constructs positively. Performance expectancy and facilitating conditions emerged as the most favorably discussed areas in the qualitative responses. Descriptive ratings showed high mean scores for motivation to use (4.61), management encouragement (4.56), ease of use (4.11), and willingness to use voluntarily (4.11). However, colleagues’ perceptions had a lower mean rating (2.72). Participants highlighted centralized data access, reduced administrative tasks, and improved clinical preparation as key benefits. At the same time, concerns were raised regarding data accuracy, questionnaire length, and the need for structured training before implementation. Conclusions: Participants reported generally positive experiences with LLCW, particularly regarding #usability and performance. However, successful implementation requires integration into existing clinical workflows and attention to training and patient engagement. Addressing these elements can support broader adoption and contribute to digital transformation in prosthetics and orthotics care.

Background: Clinical registries are critical for monitoring processes of care in diseases and driving quality improvements. However, many smaller hospitals lack the required resources to collect the necessary data to contribute to registries. Objective: This study aims to design and evaluate a data collection tool for acute stroke treatment that streamlines the collection of process data and provides tools to aid clinician users while not interfering with clinical workflow. The evaluation will identify key design requirements that facilitate prospective data collection and add value for clinicians. Methods: We developed a prototype tool for testing using Figma Pro for use on an iPad. Clinicians were recruited through convenience sampling to test the prototype’s use in a small-scale simulated clinical field experiment, during which participant were asked to think aloud and then complete a series of tasks to mimic a mock stroke treatment while inputting the required data into the prototype. Follow-up semistructured interviews were conducted to gain feedback on how the prototype integrated into the workflow and on the aspects of the prototype they felt helped and hindered their use of it. Qualitative data analysis combined review of the experiment recordings to identify the most frequent errors made during the scenario and deductive thematic analysis from the follow-up interviews to determine user needs for the following prototype iteration. The insights from the feedback identified design requirements that were implemented in the iterated design and documented to provide a reference for future product designers. Results: Three participants were recruited from 2 hospitals between April 18 and June 6, 2024, for the simulated field experiment. The scenario took 10-12 minutes, with 1.2-3.7 minutes spent using the prototype, depending on whether optional features such as the NIHSS (National Institute of Health Stroke Scale) calculator were used. The simple and condensed layout and features such as NIHSS calculators, benchmark metric timers, and the final pop-up summary received the most positive feedback from each participant. Issues identified included small target sizes causing higher error rates, lack of color in important features reducing their visibility, and grouping of mandatory and optional information field layouts leading to a disjointed flow. The key design requirements include prioritizing simple dynamic layouts, sufficient target sizes to prevent errors, useful features with clear visual cues, and prompt data feedback to facilitate seamless integration. Conclusions: A prospective data collection tool for clinicians to use during stroke treatment can add value for clinicians and, with further testing, can be integrated into workflow. The design requirements identified through this study can provide a basis for streamlining the collection of accurate data while increasing the value of the tool for users and should be considered by future product designers to add value to their software and improve user experience.

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Discover how Epic and Oracle Cerner’s latest AI tools improve EHR efficiency—and how MHHC accelerates integration.