Evaluating the effects of comprehensive simulation on central venous catheterization training: a comparative observational study.

BACKGROUND: Simulation-based training (SBT) is vital to complex medical procedures such as ultrasound guided central venous catheterization (US-IJCVC), where the experience level of the physician impacts the likelihood of incurring complications. The Dynamic Haptic Robotic Trainer (DHRT) was developed to train residents in CVC as an improvement over manikin trainers, however, the DHRT and manikin trainer both only provide training on one specific portion of CVC, needle insertion. As such, CVC SBT would benefit from more comprehensive training. An extended version of the DHRT was created, the DHRT + , to provide hands-on training and automated feedback on additional steps of CVC. The DHRT + includes a full CVC medical kit, a false vein channel, and a personalized, reactive interface. When used together, the DHRT and DHRT + systems provide comprehensive training on needle insertion and catheter placement for CVC. This study evaluates the impact of the DHRT + on resident self-efficacy and CVC skill gains as compared to training on the DHRT alone. METHODS: Forty-seven medical residents completed training on the DHRT and 59 residents received comprehensive training on the DHRT and the DHRT + . Each resident filled out a central line self-efficacy (CLSE) survey before and after undergoing training on the simulators. After simulation training, each resident did one full CVC on a manikin while being observed by an expert rater and graded on a US-IJCVC checklist. RESULTS: For two items on the US-IJCVC checklist, "verbalizing consent" and "aspirating blood through the catheter", the DHRT + group performed significantly better than the DHRT only group. Both training groups showed significant improvements in self-efficacy from before to after training. However, type of training received was a significant predictor for CLSE items "using the proper equipment in the proper order", and "securing the catheter with suture and applying dressing" with the comprehensive training group that received additional training on the DHRT + showing higher post training self-efficacy. CONCLUSIONS: The integration of comprehensive training into SBT has the potential to improve US-IJCVC education for both learning gains and self-efficacy.

Clinical Outcomes of Standardized Central Venous Catheterization Simulation Training: A Comparative Analysis.

OBJECTIVE: A standardized ultrasound-guided Internal Jugular Central Venous Catheterization (US-IJCVC) using online- and simulation-based training was first designed and then large-scale deployed at a teaching hospital institution to improve CVC surgical education. To understand the impact that the standardized training might have on patient complications, this study focuses on identifying the impact of the integration of an iteratively designed US-IJCVC training on clinical complications at a teaching hospital. DESIGN AND PARTICIPANTS: A comparative study was conducted using TriNetX, a global health research network. Using Current Procedural Terminology (CPT) codes and the International Statistical Classification of Diseases and Related Health Problems (ICD-10) codes, we identified the total number of patients with a CVC and mechanical, infectious, and thrombosis complications with and without billable ultrasound between July 1 to June 30 in 2016, 2017, and 2022. SETTING: A teaching hospital institution in Pennsylvania. RESULTS: Results showed a correlation between years and complications indicating, (1) mechanical complications billable ultrasound, (2) infectious complications billable ultrasound, and (3) thrombosis complications billable ultrasound were significantly lower with the large-scale deployment. Results also showed that (4) mechanical, infectious, and thrombosis complications with and without billable ultrasound are within the range that prior work has reported. CONCLUSION: These results indicate that there has been a decrease in mechanical, infectious, and thrombosis complications, which correlates with the US-IJCVC training large-scale deployment.

Effects of Immersion on Knowledge Gain and Cognitive Load in Additive Manufacturing Process Education.

Although the additive manufacturing (AM) market continues to grow, industries face barriers to AM adoption due to a shortage of skilled designers in the workforce that can apply AM effectively to meet this demand. This shortage is attributed to the high cost and infrastructural requirements of introducing high- barrier-to-entry AM processes such as powder bed fusion (PBF) into in-person learning environments. To meet the demands for a skilled AM workforce, it is important to explore other mediums of AM education, such as computer-aided instruction (CAI) and virtual reality (VR), which can increase access to hands-on learning experiences for inaccessible AM processes. However, limited work compares virtual and physical AM instruction or explores how the differences in immersion and presence between mediums can affect the knowledge gained and the mental effort exerted when learning about different AM processes. To address this gap in the literature, this research evaluates the use of CAI, VR, and in-person instruction in AM process education when learning about material extrusion (ME) and PBF. Our findings show that the differences in immersion and presence between CAI, VR, and in-person instruction do not have a statistically significant effect when learning about ME, but do have a significant effect when learning about PBF. Specifically, we found that VR generally yields equivalent effects in knowledge gain and cognitive load to in-person PBF education while offering advantages in both metrics over CAI learning. The findings from this work thus have significant implications for using VR as an alternative to in-person training to improve designer development in process-centric AM education of typically high-barrier-to-entry AM processes.

Identifying diversity of patient anatomy through automated image analysis of clinical ultrasounds.

PURPOSE: Central venous catheterization (CVC) carries inherent risks which can be mitigated through the use of appropriate ultrasound-guidance during needle insertion. This study aims to comprehensively understand patient anatomy as it is visualized during CVC by employing a semi-automated image analysis method to track the internal jugular vein and carotid artery throughout recorded ultrasound videos. METHODS: The ultrasound visualization of 50 CVC procedures were recorded at Penn State Health Milton S. Hershey Medical Center. The developed algorithm was used to detect the vessel edges, calculating metrics such as area, position, and eccentricity. RESULTS: Results show typical anatomical variations of the vein and artery, with the artery being more circular and posterior to the vein in most cases. Notably, two cases revealed atypical artery positions, emphasizing the algorithm's precision in detecting anomalies. Additionally, dynamic vessel properties were analyzed, with the vein compressing on average to 13.4% of its original size and the artery expanding by 13.2%. CONCLUSION: This study provides valuable insights which can be used to increase the accuracy of training simulations, thus enhancing medical education and procedural expertise. Furthermore, the novel approach of employing automated data analysis techniques to clinical recordings showcases the potential for continual assessment of patient anatomy, which could be useful in future advancements.

Shifting Perspectives: A proposed framework for analyzing head-mounted eye-tracking data with dynamic areas of interest and dynamic scenes.

Eye-tracking is a valuable research method for understanding human cognition and is readily employed in human factors research, including human factors in healthcare. While wearable mobile eye trackers have become more readily available, there are no existing analysis methods for accurately and efficiently mapping dynamic gaze data on dynamic areas of interest (AOIs), which limits their utility in human factors research. The purpose of this paper was to outline a proposed framework for automating the analysis of dynamic areas of interest by integrating computer vision and machine learning (CVML). The framework is then tested using a use-case of a Central Venous Catheterization trainer with six dynamic AOIs. While the results of the validity trial indicate there is room for improvement in the CVML method proposed, the framework provides direction and guidance for human factors researchers using dynamic AOIs.

PROGRESSIVE MEDICAL SIMULATION: AN ANALYSIS OF THE INTEGRATION OF PROGRESSIVE AND PERSONALIZED LEARNING IN CENTRAL LINE SIMULATORS.

Progressive learning gradually increases task difficulty as students advance in their education. One area that can benefit from it is medical education since it can optimize medical trainees' skill acquisition. While progressive learning can allow for skill transfer to patient encounters, personalized learning increases the efficiency and effectiveness of learning. However, it is not well understood the number of practice trials needed to reach proficiency. To evaluate whether progressive and personalized learning can enhance medical trainees' learning gains, the learning interface of the Dynamic Haptic Robotic Trainer (DHRT) for Central Venous Catheterization was assessed. Results showed that residents' performance on the DHRT did not differ based on task difficulty and residents' performance was as effective with less number of trials. The findings imply a need to integrate progressive and personalized learning on the DHRT simulator to ensure that residents are fully prepared for any patient scenario in a real-life encounter.