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Effectiveness of a Web-Based Virtual Simulation to Train Nursing Students in Suicide Risk Assessment: Randomized Controlled Investigation

The Acapela TTS server, implemented in C#, synthesizes the voice in real time with specified settings. System architecture of the virtual patient simulation. MARC: Multimodal Affective and Reactive Character; MCQ: multiple choice questionnaire. The learners played the simulation on a Windows PC installed at the Versailles Hospital through a remote desktop environment due to the restrictions on movement due to the COVID-19 pandemic.

Paul Roux, Yujiro Okuya, Cristina Morel, Mariane Soulès, Hugo Bottemanne, Eric Brunet-Gouet, Solène Frileux, Christine Passerieux, Nadia Younes, Jean Claude Martin

JMIR Serious Games 2025;13:e69347

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Consumer Data is Key to Artificial Intelligence Value: Welcome to the Health Care Future

FHIR builds upon its predecessor, the HL7 Consolidated Clinical Document Architecture (C-CDA) [25], a document-based standard used to capture a “point-in-time” snapshot of a consumer health record. Unlike C-CDA, FHIR uses modern web technology, such as RESTful APIs, JSON, and XML to enable consistent data exchange. Importantly, FHIR APIs allow for “real-time” data exchange through their discrete resource design.

James C

J Particip Med 2025;17:e68261

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Role and Use of Race in Artificial Intelligence and Machine Learning Models Related to Health

Reference 59: New creatinine- and cystatin c-based equations to estimate GFR without race

Martin C Were, Ang Li, Bradley A Malin, Zhijun Yin, Joseph R Coco, Benjamin X Collins, Ellen Wright Clayton, Laurie L Novak, Rachele Hendricks-Sturrup, Abiodun O Oluyomi, Shilo Anders, Chao Yan

J Med Internet Res 2025;27:e73996

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Estimating Nurse Workload Using a Predictive Model From Routine Hospital Data: Algorithm Development and Validation

The classifier was 72% accurate when categorizing the patients overall with a c-statistic of 0.82. An individual patient misclassification may not have much effect on the ward workload estimation as a whole, but there were no details of how to translate the patient workload categories into nurse staffing requirements for wards. Our aim was to build a predictive model using routine electronic data from 1 hospital, which might be known in real time to estimate nurse staffing requirements for wards.

Paul Meredith, Christina Saville, Chiara Dall’Ora, Tom Weeks, Sue Wierzbicki, Peter Griffiths

JMIR Med Inform 2025;13:e71666

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