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BackgroundVideo-based review is paramount for operative performance assessment but can be laborious when performed manually. Hierarchical Task Analysis (HTA) is a well-known method that divides any procedure into phases, steps, and tasks. HTA requires large datasets of videos with consistent definitions at each level. Our aim was to develop an AI model for automated segmentation of phases, steps, and tasks for laparoscopic cholecystectomy videos using a standardized HTA.MethodsA total of 160 laparoscopic cholecystectomy videos were collected from a publicly available dataset known as cholec80 and from our own institution. All videos were annotated for the beginning and ending of a predefined set of phases, steps, and tasks. Deep learning models were then separately developed and trained for the three levels using a 3D Convolutional Neural Network architecture.ResultsFour phases, eight steps, and nineteen tasks were defined through expert consensus. The training set for our deep learning models contained 100 videos with an additional 20 videos for hyperparameter optimization and tuning. The remaining 40 videos were used for testing the performance. The overall accuracy for phases, steps, and tasks were 0.90, 0.81, and 0.65 with the average F1 score of 0.86, 0.76 and 0.48 respectively. Control of bleeding and bile spillage tasks were most variable in definition, operative management, and clinical relevance.ConclusionThe use of hierarchical task analysis for surgical video analysis has numerous applications in AI-based automated systems. Our results show that our tiered method of task analysis can successfully be used to train a DL model.

In the manufacturing environments of today, human–machine systems are constituted with complex and advanced technology, which demands workers’ considerable mental workload. This work aims to design and evaluate a Graphical User Interface developed to induce mental workload based on Dual N-Back tasks for further analysis of human performance. This study’s contribution lies in developing proper cognitive analyses of the graphical user interface, identifying human error when the Dual N-Back tasks are presented in an interface, and seeking better user–system interaction. Hierarchical task analysis and the Task Analysis Method for Error Identification were used for the cognitive analysis. Ten subjects participated voluntarily in the study, answering the NASA-TLX questionnaire at the end of the task. The NASA-TLX results determined the subjective participants’ mental workload proving that the subjects were induced to different levels of mental workload (Low, Medium, and High) based on the ANOVA statistical results using the mean scores obtained and cognitive analysis identified redesign opportunities for graphical user interface improvement.

BackgroundEndoscopic sleeve gastroplasty (ESG) is a minimally invasive endoscopic weight loss procedure used to treat obesity. The long-term goal of this project is to develop a Virtual Bariatric Endoscopy (ViBE) simulator for training and assessment of the ESG procedure. The objectives of this current work are to: (a) perform a task analysis of ESG and (b) create metrics to be validated in the created simulator.MethodsWe performed a hierarchical task analysis (HTA) by identifying the significant tasks of the ESG procedure. We created the HTA to show the breakdown and connection of the tasks of the procedure. Utilizing the HTA and input from ESG experts, performance metrics were derived for objective measurement of the ESG procedure. Three blinded video raters analyzed seven recorded ESG procedures according to the proposed performance metrics.ResultsBased on the seven videos, there was a positive correlation between total task times and total performance scores (R = 0.886, P = 0.008). Endoscopists expert were found to be more skilled in reducing the area of the stomach compared to endoscopists novice (34.6% reduction versus 9.4% reduction, P = 0.01). The mean novice performance score was significantly lower than the mean expert performance score (34.7 vs. 23.8, P = 0.047). The inter-rater reliability test showed a perfect agreement among three raters for all tasks except for the suturing task. The suturing task had a significant agreement (Inter-rater Correlation = 0.84, Cronbach’s alpha = 0.88). Suturing was determined to be a critical task that is positively correlated with the total score (R = 0.962, P = 0.0005).ConclusionThe task analysis and metrics development are critical for the development of the ViBE simulator. This preliminary assessment demonstrates that the performance metrics provide an accurate assessment of the endoscopist’s performance. Further validation testing and refinement of the performance metrics are anticipated.

This study investigates the impact of user workflows and iterative decision-making on task efficiency in 3D Gaussian splatting (3DGS)-based 3D reconstruction. In current 3DGS workflows, the absence of clearly defined stages and structured processes leads users to repeatedly interpret intermediate results and revisit earlier phases, thereby increasing cognitive load and reducing efficiency. To address this issue, data were collected from ten expert users experienced in 3DGS-based workflows through semi-structured interviews and shadowing observations. To systematically decompose complex and iterative user workflows, hierarchical task analysis (HTA) was employed. The results show that the workflow can be organized into six stages: (1) Data Acquisition, (2) Project Setup, (3) 3DGS Training, (4) Result Inspection, (5) Quality Refinement, and (6) Output Utilization. User workload was primarily concentrated in the Result Inspection and Quality Refinement stages, characterized by repeated retraining and decision-making processes. Based on this analysis, four issues were identified: ambiguity in early-stage configuration, limited visibility of progress status, difficulty in identifying the causes of failure, and inefficiencies in editing operations. To address these issues, an interface design consisting of four functional areas is proposed: (1) Mode & Capture Setup, (2) Progress Management, (3) Error Review, and (4) Editing Efficiency. Evaluation by expert users indicates that the proposed interface was rated significantly higher than the existing 3DGS interface across all functional areas (p < 0.01). Higher scores were observed in Error Review (M = 4.05 vs. 1.53) and Editing Efficiency (M = 4.55 vs. 1.88) compared to the existing interface. These findings suggest that interface support for error review and editing tasks plays an important role in improving workflow usability. This study structures 3DGS workflows from a user-centered perspective and identifies the key stages where iterative decision-making is most concentrated. Based on these findings, it proposes directions for 3DGS interface design and empirically demonstrates the effectiveness of the proposed design.

Background: Ensuring the safety of patients in the operating room, through the monitoring and prevention of adverse events is a central priority of healthcare delivery. In the professionalization of operating room nurses, the processes of identifying, assessing, developing, monitoring, and certifying transversal competencies are crucial. While national and international frameworks have attempted to define such competencies, they often vary in scope and remain inconsistently integrated into education and clinical practice. There is, therefore, a need for a comprehensive and structured identification of transversal competencies relevant to both perioperative and perianesthesiological nursing roles. Objectives: To formulate a validated and structured repertoire of transversal competencies demonstrated by operating room nurses in both perioperative and perianesthesiological contexts. Methods: A qualitative descriptive design was adopted, combining shadowed observation with Hierarchical Task Analysis (HTA). A convenience sample of 46 participants was recruited from a university and a public hospital in Italy. Data were collected between September 2021 and June 2023 and analyzed using content analysis and data triangulation. Results: Through a qualitative, inductive and iterative approach the study identified 15 transversal competencies, 50 sub-competencies, and 153 specific tasks and activities. Specifically, operating room nurses working in perioperative and perianesthesiological roles presented the following transversal competencies: communication and interpersonal relationships, situation awareness, teamwork, problem solving and decision-making, self-awareness, coping with stressors, resilience and fatigue management, leadership, coping with emotions, task and time management, ethical and sustainable thinking, adaptation to the context, critical thinking, learning through experiences, and data, information and digital content management. Each competency was associated with specific tasks observed. Conclusions: This framework complements the existing repertoire of technical-specialist competencies by integrating essential transversal competencies. It serves as a valuable tool for the assessment, validation, and certification of competencies related to patient and professional safety, emotional well-being, relational dynamics, and social competencies. The findings underscore the need for academic institutions to revise traditional training models and embed transversal competencies in both undergraduate and postgraduate nursing education.

Remote towers are increasingly deployed at small-to-medium airports globally for cost efficiency, yet safety optimization for large airport remote apron control remains underexplored. This study proposes a human error identification framework for air traffic controllers (ATCOs) in large airport remote apron operations. Using hierarchical task analysis (HTA), a cognitive-behavioral model, and the technique for retrospective analysis of cognitive errors (TRACEr), we analyzed error probability and severity through field research. Key findings reveal critical divergences. Memory functions showed the highest error probability, while perception errors caused the most severe outcomes. Working memory errors were most prevalent, but visual detection errors were most severe. Attention deficits were most frequent, while spatial confusion and information integration failures exceeded severity thresholds. Personal factors dominated performance-shaping factors, with low vigilance and equipment unavailability as primary high-risk conditions. This research provides an error identification checklist and analysis methodology to enhance human performance and aviation safety in remote apron control.

BackgroundTraditionally, hierarchical task analysis (HTA) in surgery examines observable disruption in a predefined set of tasks as performed, rather than examining the ergonomics requirements, which may predispose surgical teams to act erroneously. This research aims to address this gap in the literature. It develops a HTA protocol taking into consideration surgical team actions, observable external disruption, internal disruption, and ergonomic goals required for safer conducting procedures. Laparoscopic radical prostatectomy (LRP) is selected as a case.MethodsThis research involved observations inside operating rooms (ORs) of three large teaching hospitals in Australia and China. Two rounds of observations are conducted: observations for developing HTA, and observations after presenting the developed HTA among surgical teams. The traditional HTA format is expanded to include two additional columns: technical considerations and ergonomics considerations. Two groups are formed from the observed LRPs. LRPs in the first group were conducted with no regard to the specified ergonomic goals and associated ergonomic features, and the second are conducted with the surgical teams attempting to follow specified ergonomic goals and features as prescribed in HTA. Careful attempt is required to select procedures such that the total operative times for both groups are approximately equal (± 5%).ResultsBetween March 2016 and November 2017, a total of 29 LRPs were observed, and a HTA developed. The results reveal significant reduction (43%) in the total external disruptive events and approximately 58% reduction in the internal disruptive events in LRPs conducted with HTA requirements.ConclusionsThe developed HTA appears to have some utility, but needs evaluation in larger studies. It can potentially be used as a training aid, and as a checklist for evaluating surgical performance.

Motor imagery and mental practice are important for the acquisition and mastery of surgical skills. The success of this technique relies on the use of a well-developed mental script. In this study, we shared how we developed a mental script for basic micro suturing training by using a low-fidelity rubber glove model. This study applied the design and development research framework. Five expert surgeons developed a mental script by performing a cognitive walkthrough to repair a vertical opening in a rubber glove model, followed by hierarchical task analysis. A draft script was created, and its face and content validity assessed with a checking-back process. Twenty-eight surgeons used the Mental Imagery Questionnaire (MIQ) to assess the validity of the final script. The process of developing the mental script is detailed. The assessment by the expert panel showed the mental script had good face and content validity. The mean overall MIQ score was 5.2±1.1 (standard deviation), demonstrating the validity of generating mental imagery from the mental script developed in this study for micro suturing in the rubber glove model. The methodological approach described in this study is based on a design and development research framework to teach surgical skills. This model is inexpensive and easily accessible, addressing the challenges of reduced opportunities to practise surgical skills. However, although motor skills are important, the surgeon's other non-technical expertise is not addressed with this model. Thus, this model should act as one surgical training approach, but not replace it.

This study presents a systematic literature review of 307 peer-reviewed articles on collision avoidance approaches regarding the integration of maritime autonomous surface ships (MASSs) in coastal waters supply chains. The bibliographic data were retrieved from the ISI Web of Science Database and analyzed using Bibliometrix (version 4.3.3) in R and VOSviewer (version 1.6.20) to map the intellectual, thematic, and network structure of the research area. Three main research clusters were revealed through bibliographic coupling analysis: (1) autonomous collision risk management; (2) methodological approaches to maritime autonomy; and (3) adaptive maritime safety modeling. Content analysis of the identified research clusters enabled the development of a 68-item hierarchical task analysis (HTA) framework for MASS collision avoidance across three operational scenarios: (1) ship-to-object, (2) ship-to-ship, and (3) multi-ship. The results provide a comprehensive overview of the current state of research, identify methodological and safety interdependencies in autonomous navigation, and offer an organized and structured perspective to support the safer and more efficient integration of MASSs into coastal waters supply chains.

In the domain of Advanced Air Mobility (AAM), Simplified Vehicle Operations (SVOs) promise a reduction in handling complexity and training time for pilots. Designing a usable human–machine interface (HMI) for pilots of SVO-enabled aircraft requires a deep understanding of task and user requirements. This paper describes the results of two user research methods to gather these requirements. First, a traditional Helicopter Emergency Medical Service (HEMS) mission was examined using a Hierarchical Task Analysis (HTA). The findings were used to formulate a theoretical HTA for a single-piloted electric Vertical Take-Off and Landing (eVTOL) system in such a scenario. In the second step, qualitative interviews with seven subject matter experts (pilots and paramedic support) in HEMS operations produced vital user requirements for HMI development. Key findings emphasize the necessity of a simplified information presentation and collision avoidance support in the HMI.