--- title: 'NOTSS: Non-Technical Skills for Surgeons' url: https://www.emergentmind.com/topics/non-technical-skills-for-surgeons-notss type: topic --- # NOTSS: Non-Technical Skills for Surgeons Non-Technical Skills for Surgeons (NOTSS) comprise a structured taxonomy and assessment framework for those behavioral, cognitive, and interpersonal competencies essential to surgical safety and team performance that are not strictly technical. Developed over the past two decades, NOTSS has become the reference framework for both research and training in surgical teamwork, leadership, situation awareness, decision making, and communication. Recently, computer vision (CV), artificial intelligence (AI), and large language model (LLM)-based analytics have enabled quantitative and scalable measurement of NOTSS elements using both kinematic and linguistic data sources, with particular activity in specialities such as cardiothoracic and laparoscopic surgery [2402.11288, 2601.13406]. ## 1. Taxonomy and Components of NOTSS The NOTSS framework, as adopted in current surgical safety research, consists of five principal categories [2402.11288]: - **Situation Awareness**: Monitoring the operating environment, anticipating future states, recognizing and responding to cues. Supported by sub-skills such as anticipatory ability and cognitive flexibility. - **Decision Making**: Selecting and executing an appropriate course of action under time pressure or uncertainty. Key subcomponents include mental readiness and action planning. - **Communication and Interaction**: Clarity of speech, closed-loop communication, checking shared understandings. - **Co-operation and Team Skills**: Sharing information, mutual monitoring, supporting and backup behaviors. - **Leadership and Managerial Skills**: Setting and maintaining standards, resource management, assertiveness, and conflict resolution. The NOTECHS scale, a sister instrument, extends the same principles and has been validated as a behavioral marker set [71, 72 in 2402.11288]. Within simulation and training environments, current practice also identifies specific subskills (e.g., use of communicative gestures for structuring team action) as targets for assessment and feedback [2402.11288, 2601.13406]. ## 2. Assessment Methodologies: Classical and Algorithmic ### 2.1 Human-Rated NOTSS Scoring Historically, NOTSS assessment has relied on direct observation or video review by trained clinical raters, using standardized behavioral marker forms. Reliability is quantified via inter-rater statistics, typically the Intraclass Correlation Coefficient (ICC) or Cohen’s κ: $$ ICC = \frac{MS_B - MS_W}{MS_B + (k-1)MS_W} $$ where $MS_B$ is between-videos mean square, $MS_W$ is within-videos mean square, $k$ is the number of raters [2402.11288]. Yule et al. [72 in 2402.11288] demonstrated the construct and criterion validity of human-rated NOTSS, reporting ICCs “in the good to excellent range,” that is, ICC ≥ 0.75. ### 2.2 Computer Vision and AI Approaches Recent work proposes a range of CV/AI model families for objective, automated NOTSS scoring. These may be grouped as follows [2402.11288]: **Pose Estimation Frameworks**: - Markerless 2D/3D pose estimation using deep networks: OpenPose/Part-Affinity Fields, DeeperCut, DeepLabCut (supporting multi-person and multi-instrument tracking). - RGB-D (depth sensor) systems for coarse 3D head and upper-body tracking. - Hybrid models combining pose skeletons with inertial sensor data under gloves. **Kinematic Indicators for NOTSS Domains**: | NOTSS Domain | Kinematic Proxy/Feature | |------------------------|------------------------------------------------------| | Situation Awareness | Head pose orientation (yaw/pitch/roll) streams $\{h(t)\}$; gaze-proxy (face-ROI distance) | | Communication/Interaction | Gesture amplitude and velocity; response latency between gestures | | Decision Making/Team Coordination | Idle-time in bimanual activity; hand-trajectory velocity cross-correlation (“synchrony”) | **AI Classifier Architectures**: - Skeleton-based graph neural networks (GCNs) or LSTMs: time-sequence of joint coordinates $X_t \in \mathbb{R}^{J \times 2}$, velocity $v_j(t)$, and joint angles $\theta_{jk}(t)$ as input, regressing to domain-specific NOTSS scores. - Two-stream convolutional networks: one stream ingests RGB, the other pose/optical flow, jointly classifying high/low performance per NOTSS category. - Explainable-AI: attention layers localize the spatiotemporal features driving each NOTSS score. No end-to-end, peer-reviewed, AI-driven NOTSS validation pipeline has yet been reported. Closest analogues are surgical technical skill classifiers with accuracies ≥80% [65 in 2402.11288]. ## 3. LLM and VR-Driven NOTSS Analytics The emergence of language model analytics and immersive simulation platforms such as the Virtual Operating Room Team Experience (VORTeX) enables team-based NOTSS evaluation using transcript analysis [2601.13406]. **Prompt Engineering and Category Mapping**: VORTeX uses a composite prompt embedding the four primary NOTSS categories—“Situational Awareness,” “Decision Making,” “Communication and Teamwork,” “Leadership”—as explicit definitions within the task for the LLM. The LLM is instructed to (a) tag dialogue evidence, (b) self-verify outputs, and (c) return a directed JSON interaction graph representing the session. **Network Science Metrics**: For each participant node $v_i$, directed edges $e_{ij}$ are established when NOTSS behavior of category $c$ occurs from $i \to j$. Derived metrics include: - In-degree: $d_{in}(i) = \sum_{j}A_{ji}$ - Out-degree: $d_{out}(i) = \sum_{j}A_{ij}$ - Weighted degree: $d_{tot}(i) = d_{in}(i) + d_{out}(i)$ - Clustering coefficient: $C_i = \frac{2 \cdot \#\,\text{triangles through }i}{k_i(k_i-1)}$ - Betweenness centrality: $b(i) = \sum_{s \ne i \ne t} \frac{\sigma_{st}(i)}{\sigma_{st}}$ - Hierarchy index: $H = 1 - \frac{2\sum_{i