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.

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.

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.

Due to nursing shortages, an ageing population and increasing care demand, there is a growing interest in parenteral medication administration at home (PMAaH), comprising the administration of parenteral medication in the home situation of patients. The operational design of such PMAaH care pathways is complex, resulting in many variations of adoptions, showing a need for a quality framework. Although quality indicators (QIs) have been proposed to monitor the quality of specific care pathways, a generic quality framework for all types of PMAaH is lacking. Therefore, this study proposes a generic quality set for PMAaH, which includes structure and process QIs, to benchmark and redesign PMAaH care pathways to ensure high quality.

A generic QI set was developed for PMAaH using a systematic RAND appropriateness method adapted at the third phase. This method consisted of a scoping review to identify indicators, an expert panel rating phase including an online questionnaire and subsequent panel meeting to assess the appropriateness of the indicators and a retrospective practice testing to evaluate the feasibility, clarity and measurability of the indicators. After the practice testing, which consisted of an online questionnaire where experts could indicate the implementation state of all indicators in their hospital, a third expert panel adjusted the set to increase the likelihood of implementation in practice.

The experts, all healthcare professionals involved in PMAaH processes, were recruited using the snowball sampling technique from three large Dutch, teaching hospitals. Subsequently, a practice testing by self-assessment was conducted in seven large Dutch teaching hospitals.

17 and seven healthcare professionals with diverse backgrounds participated in the online questionnaire and panel meeting, respectively.

The scoping review resulted in 36 QIs for PMAaH. After two expert panel rating rounds (online questionnaire and panel meeting), two indicators were removed: a QI related to travel distance policy since it was irrelevant and redundant, and a QI stating that a clinician should take the lead in a PMAaH-team, which was deemed too restrictive. After the practice testing, two QIs were removed: a QI related to clinical response documentation, which was unclear for the practice testing respondents and already covered by other QIs, and a QI related to survival documentation, which was deemed infeasible and undesirable to measure this differently than other patients by the third expert panel.

The final set consists of 32 indicators (of which 15 were structure indicators and 17 were process indicators). The final set predominately includes QIs that are aimed at patient safety but also QIs focusing on the working conditions of the healthcare workers. 17.6% of the QIs are currently fully implemented in general in all seven hospitals. The practice testing revealed that operational QIs are more frequently implemented in practice than systemic QIs and that a structured quality assurance programme is needed in the hospitals.

This study proposes a generic quality set for PMAaH that hospitals can use to redesign and benchmark PMAaH care pathways to assure high quality. The practice testing confirmed that there is a need for this structured quality set.