Dataset Information

The effect of repeated full immersion simulation training in ureterorenoscopy on mental workload of novice operators.

ABSTRACT:

Background

Difficult surgical procedures may result in a higher mental workload, leading to increased fatigue and subsequent errors. This study was aimed to investigate the effect of repeated simulation training in ureterorenoscopy in a high-fidelity setting on the performance and mental workload of novice operators.

Methods

Medical students voluntarily participated in the present simulation study. After a didactic and video-based lecture, they underwent simulation training involving a renal stone case, including a rigid cystoscope component (task 1, performing a WHO checklist, assembling a scope, and insertion of a guide-wire and an access sheath after examining the bladder) and a flexible ureterorenoscope component (task 2, retrieving a stone located in the upper calyx using a basket after inspecting the upper, middle, and lower calyx). Training was performed in a mock operating theater. Technical skills were assessed by one author (an experienced urologist) onsite using an Objective Structured Assessment of Technical Skills (OSATS) score at each training session. The mental workload was subjectively evaluated by the National Aeronautics and Space Administration Task Load Index (NASA-TLX) questionnaire after each training session.

Results

Seventeen students completed a minimum of 6 training sessions (male: female?=?10: 7, median age of 22) over a median of 21?days (range, 10-32). In both tasks 1 and 2, the OSATS score improved over the 6 sessions with evidence of plateauing (MANOVA model, task 1: p ConclusionsUnder repeated simulation training in ureterorenoscopy in a high-fidelity setting, participants showed a continual decrease of the mental workload, while the improvement of technical skills reached a plateau over the 6 sessions. Our study showed the important benefit of simulation training to reduce the mental workload by repeated scenario training before actual clinical practice.

SUBMITTER: Abe T

PROVIDER: S-EPMC6704721 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The effect of repeated full immersion simulation training in ureterorenoscopy on mental workload of novice operators.

Abe Takashige T Dar Faizan F Amnattrakul Passakorn P Aydin Abdullatif A Raison Nicholas N Shinohara Nobuo N Khan Muhammad Shamim MS Ahmed Kamran K Dasgupta Prokar P

BMC medical education 20190822 1

<h4>Background</h4>Difficult surgical procedures may result in a higher mental workload, leading to increased fatigue and subsequent errors. This study was aimed to investigate the effect of repeated simulation training in ureterorenoscopy in a high-fidelity setting on the performance and mental workload of novice operators.<h4>Methods</h4>Medical students voluntarily participated in the present simulation study. After a didactic and video-based lecture, they underwent simulation training involv ...[more]

PMID: 31438934

Similar Datasets

Project description:We studied the capability of a Hybrid functional neuroimaging technique to quantify human mental workload (MWL). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter n-back task, a standard experimental paradigm related to working memory (WM). The level of MWL was parametrically changed by variation of n from 0 to 3. Nineteen EEG channels were covering the whole-head and 19 fNIRS channels were located on the forehead to cover the most dominant brain region involved in WM. Grand block averaging of recorded signals revealed specific behaviors of oxygenated-hemoglobin level during changes in the level of MWL. A machine learning approach has been utilized for detection of the level of MWL. We extracted different features from EEG, fNIRS, and EEG+fNIRS signals as the biomarkers of MWL and fed them to a linear support vector machine (SVM) as train and test sets. These features were selected based on their sensitivity to the changes in the level of MWL according to the literature. We introduced a new category of features within fNIRS and EEG+fNIRS systems. In addition, the performance level of each feature category was systematically assessed. We also assessed the effect of number of features and window size in classification performance. SVM classifier used in order to discriminate between different combinations of cognitive states from binary- and multi-class states. In addition to the cross-validated performance level of the classifier other metrics such as sensitivity, specificity, and predictive values were calculated for a comprehensive assessment of the classification system. The Hybrid (EEG+fNIRS) system had an accuracy that was significantly higher than that of either EEG or fNIRS. Our results suggest that EEG+fNIRS features combined with a classifier are capable of robustly discriminating among various levels of MWL. Results suggest that EEG+fNIRS should be preferred to only EEG or fNIRS, in developing passive BCIs and other applications which need to monitor users' MWL.

Project description:Background:Immersion is important for simulation-based education; however, questionnaire-based instruments to measure immersion have some limitations. The aim of the present work is to develop a new instrument to measure immersion among participants in healthcare simulation scenarios. Methods:The instrument was developed in four phases: trigger identification, content validity scores, inter-rater reliability analysis and comparison with an existing immersion measure instrument. A modified Delphi process was used to develop the instrument and to establish validity and reliability. The expert panel consisted of 10 researchers. All the researchers in the team had previous experience of simulation in the health and/or fire and rescue services as researchers and/or educators and simulation designers. To identify triggers, the panel members independently screened video recordings from simulation scenarios. Here, a trigger is an event in a simulation that is considered a sign of reduced or enhanced immersion among simulation participants. Results:The result consists of the Immersion Score Rating Instrument (ISRI). It contains 10 triggers, of which seven indicate reduced and three enhanced immersion. When using ISRI, a rater identifies trigger occurrences and assigns them strength between 1 and 3. The content validity analysis shows that all the 10 triggers meet an acceptable content validity index for items (I-CVI) standard. The inter-rater reliability (IRR) among raters was assessed using a two-way mixed, consistency, average-measures intra-class correlation (ICC). The ICC for the difference between weighted positive and negative triggers was 0.92, which indicates that the raters are in agreement. Comparison with results from an immersion questionnaire mirrors the ISRI results. Conclusions:In conclusion, we present a novel and non-intrusive instrument for identifying and rating the level of immersion among participants in healthcare simulation scenarios.

Dataset Information

The effect of repeated full immersion simulation training in ureterorenoscopy on mental workload of novice operators.

Background

Methods

Results

Publications

The effect of repeated full immersion simulation training in ureterorenoscopy on mental workload of novice operators.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure