{
  "abstract": "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.",
  "authors": [
    {
      "affiliations": [
        "Institute for Anesthesiology and Perioperative Medicine, University Hospital Zurich, Zurich, Switzerland"
      ],
      "name": "Cynthia Alexandra Hunn"
    },
    {
      "affiliations": [
        "Institute for Anesthesiology and Perioperative Medicine, University Hospital Zurich, Zurich, Switzerland"
      ],
      "name": "Hannes Bruns"
    },
    {
      "affiliations": [
        "Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA"
      ],
      "name": "Sebastian Sahli"
    },
    {
      "affiliations": [
        "Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA"
      ],
      "name": "Luca Wachtendorf"
    },
    {
      "affiliations": [
        "Department of Anaesthesiology, Heidelberg University, Heidelberg, BW, Germany"
      ],
      "name": "Johannes Schäfer"
    },
    {
      "affiliations": [
        "Department of Anesthesiology and Intensive Care, TUM, Munich, BY, Germany"
      ],
      "name": "Stefan Schwerin"
    },
    {
      "affiliations": [
        "Department of Anesthesiology, Universitätsklinikum Bonn, Bonn, NRW, Germany"
      ],
      "name": "Achilles Delis"
    },
    {
      "affiliations": [
        "Seminar for Statistics, ETH Zurich, Zürich, ZH, Switzerland"
      ],
      "name": "Markus Kalisch"
    },
    {
      "affiliations": [
        "Seminar for Statistics, ETH Zurich, Zürich, ZH, Switzerland"
      ],
      "name": "Gaspar Dugac"
    },
    {
      "affiliations": [
        "Institute for Anesthesiology and Perioperative Medicine, University Hospital Zurich, Zurich, Switzerland"
      ],
      "name": "Arend Rahrisch"
    },
    {
      "affiliations": [
        "Department of Anesthesiology and Intensive Care, TUM, Munich, BY, Germany"
      ],
      "name": "Max Ebensperger"
    },
    {
      "affiliations": [
        "Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA"
      ],
      "name": "Arian Karimitar"
    },
    {
      "affiliations": [
        "Department of Anesthesiology, Universitätsklinikum Bonn, Bonn, NRW, Germany"
      ],
      "name": "Gregor Massoth"
    },
    {
      "affiliations": [
        "Department of Anaesthesiology, Heidelberg University, Heidelberg, BW, Germany"
      ],
      "name": "Christopher Neuhaus"
    },
    {
      "affiliations": [
        "Department of Computer Science, ETH Zurich, Zürich, ZH, Switzerland"
      ],
      "name": "Alina Dubatovka"
    },
    {
      "affiliations": [
        "Institute for Anesthesiology and Perioperative Medicine, University Hospital Zurich, Zurich, Switzerland"
      ],
      "name": "Christoph B Nöthiger"
    },
    {
      "affiliations": [
        "Institute for Anesthesiology and Perioperative Medicine, University Hospital Zurich, Zurich, Switzerland"
      ],
      "name": "G. Gasciauskaite"
    },
    {
      "affiliations": [
        "Institute for Anesthesiology and Perioperative Medicine, University Hospital Zurich, Zurich, Switzerland"
      ],
      "name": "Tadzio Raoul Roche"
    },
    {
      "affiliations": [
        "Institute for Anesthesiology and Perioperative Medicine, University Hospital Zurich, Zurich, Switzerland"
      ],
      "name": "David W Tscholl"
    }
  ],
  "title": "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)",
  "uid": "cf062c8c-edfc-5da0-9374-634d5ec08ce5"
}