VORTeX: Virtual OR Team Experience

• VORTeX is a platform that combines multi-user VR simulation, surround-video telepresence, and egocentric neural rendering for realistic operating room team training.
• The framework integrates advanced LLM-driven behavioral analytics to objectively assess non-technical skills such as communication, decision making, and leadership.
• It uses quantitative metrics and dynamic crisis scenarios to validate team performance improvements and operational efficiency in surgical training.

The Virtual Operating Room Team Experience (VORTeX) encompasses a confluence of advanced simulation, team-based training, immersive reality, and analytically rigorous behavioral assessment for operating room (OR) teams. At its core, VORTeX leverages multi-user virtual reality (VR) environments integrated with LLM analytics, augmented by developments in telepresence, surround video, and egocentric neural rendering, to facilitate scalable training, objective assessment, and in-depth replay of non-technical team competencies under realistic surgical crisis conditions (Barker et al., 19 Jan 2026, Taweel et al., 2023, Zhang et al., 6 Oct 2025).

1. System Architecture and Technological Foundations

VORTeX platforms are realized through distinct but complementary systems, notably the immersive simulation and behavioral analytics framework of (Barker et al., 19 Jan 2026), the surround-video-based telepresence pipeline SURVIVRS (Taweel et al., 2023), and neural 3D scene capture and egocentric synthesis as in EgoSurg (Zhang et al., 6 Oct 2025).

• Immersive Team VR Simulation: Multi-user VR is built on consumer-grade head-mounted displays (HMDs) and hand controllers configured for distinct OR roles (surgeon, nurse, anesthesiologist), supporting spatialized audio, high-frequency motion/gaze tracking, and role-specific interactive stations. Real-time physiologic engines simulate dynamic vital signs and responses to pharmacologic or procedural interventions (Barker et al., 19 Jan 2026).
• Surround Video Telepresence: 360° 8K surgery-room video (e.g., from Insta360 Pro) feeds into VR rendering engines (Unity/OpenXR), enabling live panoramic or recorded navigation. Multiple synchronized views (site, vitals) and real-time annotation panels provide multimodal situational awareness and support bidirectional communication (Taweel et al., 2023).
• Egocentric 3D Scene Synthesis: EgoSurg utilizes four wall-mounted stereo RGB cameras and performs multi-view triangulation, 3D Gaussian Splatting (3DGS), and diffusion-based novel view enhancement. This enables arbitrary, time-indexed, role-specific replays of any OR team member's visual perspective through volumetric neural rendering (Zhang et al., 6 Oct 2025).

2. Behavioral Analytics: LLM-Driven Evaluation

A unique distinguishing feature is the LLM-driven pipeline for structured behavioral analysis and metrics computation (Barker et al., 19 Jan 2026):

• Dialogue Capture and Processing: All team dialogue is captured as audio, processed into time-stamped, speaker-tagged transcripts via local automatic speech recognition (ASR) and diarization.
• NOTSS Rubric and Prompting: The pipeline adapts the Non-Technical Skills for Surgeons (NOTSS) behavioral rubric, operationalizing four domains—Situational Awareness, Decision Making, Communication & Teamwork, Leadership—into composite LLM prompts. Sequence: transcript → prompt with specific extraction and schema instructions → LLM generates JSON describing directed behavior edges including source, target, NOTSS domain, timestamp, and rationale.
• Interaction Graph Construction: Output data is structured as a directed network (nodes: team members; edges: NOTSS-coded behaviors) supporting subsequent network metric analysis.

3. Quantitative Metrics and Mathematical Formulations

Structured data from the LLM pipeline is ingested by an analytics dashboard which computes reproducible network-based metrics (Barker et al., 19 Jan 2026):

• Communication Frequency: Count of directed behavior edges per participant.
• Response Latency: Temporal interval between initiating behavior and corresponding responses.
• Hierarchy Index (HI): $HI_i = \frac{d_i^{out}}{d_i^{in}+\epsilon}$, where $d^{out}_i$ and $d^{in}_i$ are out/in-degree centralities, and $\epsilon$ is a small positive constant.
• Clustering Coefficient (symmetrized adjacency): $$C_i = \frac{2 \times \#\{\text{triangles through } i\}}{k_i(k_i-1)}$$.
• Role Validation: Interaction network analysis consistently reproduces expected operative hierarchies: surgeons as high-in-degree integrators, nurses as initiators (high out-degree), and anesthesiologists as balanced intermediaries.

A representative interaction graph and derived adjacency matrix, metrics, and clustering statistics are explicitly provided in (Barker et al., 19 Jan 2026).

4. Scenario Engineering and Realism

VORTeX platforms actualize high-stress, realistic team scenarios to elicit and measure targeted non-technical skills:

• Crisis Event Scenarios: Validated laparoscopic emergencies such as pneumothorax (sudden hypoxia, raised airway pressures) and intra-abdominal bleeding (progressive hemodynamic deterioration) are engineered with physiologic and environmental fidelity, dynamic waveform generation, and spatialized ambient noise (Barker et al., 19 Jan 2026).
• Remote Telepresence and Annotation: Systems such as SURVIVRS enable remote experts to provide live or recorded guidance, overlaid annotations, and gesture-based cues—mapped to VR in real time—with frame-synchronized video, voice, and interaction streams (Taweel et al., 2023).
• Egocentric Replay: EgoSurg reconstructs and replays any participant's visual field via neural rendering, supporting immersive post-hoc analysis and training from both panoramic and perspective-corrected viewpoints (Zhang et al., 6 Oct 2025).

5. Performance, Usability, and Validation

Multi-dimensional evaluation metrics are employed across VORTeX platforms:

• System Performance: End-to-end latency for VR telepresence is targeted at $L_{total} \leq 200$ ms, with per-view rendering in neural pipelines achieving latencies < 20 ms and high frame rates (>150 fps static, ~50 ms dynamic view refinement) (Taweel et al., 2023, Zhang et al., 6 Oct 2025).
• Quantitative Usability: System Usability Scale (SUS), Slater-Usoh-Steed presence scores, and NOTSS-item Likert responses display high usability, immersion, and perceived training value, e.g., SUS-style items in (Barker et al., 19 Jan 2026) (“easy to use” 3.58±1.09; “improves NTS” 4.42±0.67), with high presence and annotation tool utility in SURVIVRS (Taweel et al., 2023).
• Pilot Validation: In multi-institutional and conference settings, distributed user configurations (local/remote, consumer-HMD, server separation) are feasible; rapid acclimation occurs even among those with little prior VR experience (Barker et al., 19 Jan 2026).
• Reconstruction Fidelity: EgoSurg achieves PSNR 17.8±2.0 dB and SSIM 0.766±0.026 for egocentric view synthesis, outperforming depth-reprojection and naïve baseline models by substantial margins (Zhang et al., 6 Oct 2025).

6. Privacy, Scalability, and Implementation Considerations

Design decisions prioritize privacy, scalability, and cost-effectiveness:

• Privacy Compliance: All data and computational processes reside on institutional servers, with no external cloud processing for LLM, end-to-end IPSec-encrypted VPN tunnels for client–server communication, and open-source reasoning for auditability (Barker et al., 19 Jan 2026).
• Resource Efficiency: Consumer-grade VR hardware and separation of client/server workloads enable distributed simulation and analytics; lightweight annotation and telepresence architectures minimize logistical barriers (Taweel et al., 2023).
• Data Interoperability: 3DGS scenes, timeline meta-data, and interaction graphs are exported in standard formats compatible with Unity/Unreal or other analytic engines for further visualization or review (Zhang et al., 6 Oct 2025).

7. Future Directions and Expansions

Current research proposes several near-term and long-term enhancements:

• Comprehensive Multimodal Analytics: Plans include integrating eye-tracking, fine hand gesture data, real-time physiological signals (e.g., galvanic skin response, heart rate), and direct HL7/FHIR data feeds for interactive dashboards (Barker et al., 19 Jan 2026, Taweel et al., 2023, Zhang et al., 6 Oct 2025).
• Scenario and Curriculum Expansion: Scenario libraries will increase and adapt dynamically to user performance, enabling adaptive crisis injection and skill progression mapping.
• Multi-Institutional and Longitudinal Studies: Proposed large-scale validation with intact clinical teams and incorporation into competency-based curricula for longitudinal NTS tracking (Barker et al., 19 Jan 2026).
• Advanced VR/AR Integration: Efforts are underway to develop augmented reality overlays, avatar-based telepresence, and real-time decision support tools, as well as counterfactual scenario optimization for surgical layout or training interventions (Taweel et al., 2023, Zhang et al., 6 Oct 2025).

VORTeX thus defines a comprehensive, analytically robust framework for immersive simulation, objective assessment, and scalable team-based training in operative medicine, synthesizing advances in VR, telepresence, behavioral analytics, and neural scene synthesis (Barker et al., 19 Jan 2026, Taweel et al., 2023, Zhang et al., 6 Oct 2025).