Treffer: Evaluation of visual patient predictive for enhancing level 3 situation awareness: protocol for a multicentre randomised computer-based simulation and diagnostic accuracy study (true positive rate, precision, average lead time).

Introduction: Visual Patient Predictive (VPP) is an AI-based extension of the Visual Patient Avatar (VPA) that integrates deep learning models to predict upcoming vital sign deviations and display them as dashed visual elements. By explicitly showing anticipated changes, the system aims to support level 3 situation awareness-the projection of future patient states. This multicentre simulation study will evaluate whether predictive algorithms and visualisations integrated into the VPA (resulting in VPP) improve clinicians' ability to anticipate critical vital sign changes compared with conventional number-based and waveform-based monitoring and examine its effects on decision-making, confidence, workload and user acceptance.
Methods and Analysis: This investigator-initiated, randomised, within-subjects crossover, computer-based simulation trial will be conducted at five academic centres in Switzerland, Germany and the United States. Medical professionals from anaesthesiology departments will complete scenario-based prediction tasks using both VPP (as the index test) and conventional monitoring (as the reference standard) in randomised order, with the same participant evaluating both modalities and the identical underlying clinical scenario used in each condition, following video-based training and a learnability test. The primary outcome is recall (true positive rate) of vital sign deviation predictions. Secondary outcomes include average lead time, precision, prediction confidence, number and correctness of proposed interventions, perceived workload (NASA-TLX) and qualitative usability feedback. Quantitative data will be analysed using a logistic generalised linear mixed model with random intercepts for centre and participant, and a random slope for the intervention effect. Qualitative interviews will undergo thematic analysis.
Ethics and Dissemination: The leading ethics committee (Zurich, Switzerland; BASEC-Req-2023-00465) reviewed and approved the study protocol. Ethics committees at the other participating centres have obtained their respective approvals or waivers. Bonn: 2025-144-BO, Boston: 2025P000501, Heidelberg: S-376/2025, Munich: 2025-357 W-CB. As this simulation study involves only healthcare professionals performing prediction tasks based on simulated vital sign scenarios-without collection of patient data or any medically relevant personal data-it does not constitute human subjects research under applicable regulations. Study results will be disseminated through peer-reviewed publications and presentations at scientific conferences.
(© Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group.)

Competing interests: CBN, TRR and DWT are inventors of the Visual Patient Predictive technology. This technology has been licensed by the University of Zurich to Philips. In accordance with University of Zurich regulations, inventors are entitled to a share of potential future revenues in the event of successful commercialization. CBN and DWT are also inventors of the Visual Patient Avatar technology, on which Visual Patient Predictive is based, and receive royalties related to this technology. Authors CAH, CBN, TRR and DWT have received honoraria for lectures from Philips. Authors CAH, AD, TRR and DWT have received research funding from Philips through joint development agreements with their respective universities. All funding and remuneration were managed via institutional agreements, and Philips had no role in the study design, data collection, data analysis, data interpretation, or manuscript preparation.