Granados, A; Low-Beer, N; Higham, J; Kneebone, R; Bello, F
(2018)
Real-Time Visualization and Analysis of Clinicians’ Performance During Palpation in Physical Examinations.
IEEE Trans Biomed Eng, 65 (9).
pp. 2042-2051.
ISSN 1558-2531
https://doi.org/10.1109/TBME.2017.2780982
SGUL Authors: Higham, Jennifer Mary
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Abstract
Objective: Motivated by the fact that palpation skills are challenging to learn and teach, particularly during Digital Rectal Examinations (DRE), and the lack of understanding of what constitutes adequate performance, we present a visualization and analysis system that uses small position and pressure sensors located on the examining finger, allowing the quantitative analysis of duration, steps, and pressure applied. Methods: The system is first described, followed by an experimental study of 20 experts from four clinical specialties performing ten DREs each on a benchtop model using the proposed system. Analysis of the constitutive steps was conducted to improve understanding of the examination. A Markov model representing executed tasks and analysis of pressure applied is also introduced. Results: The proposed system successfully allowed the visualization and analysis during the experimental study. General practitioners and nurses were found to execute more tasks compared to urologists and colorectal surgeons. Urologists executed the least number of tasks and were the most consistent group compared to others. Conclusion: The ability to “see through” allowed us to better characterize the performance of experts when conducting a DRE on a benchtop model, comparing the performance of relevant specialties, and studying executed tasks and the pressure applied. The Markov model presented summarizes task execution of experts and could be used to compare the performance of novices against that of experts. Significance: This approach allows for the analysis of performance based on continuous sensor data recording that can be easily extended to real subjects and other types of physical examinations.
Item Type: | Article | |||||||||
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Additional Information: | This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/ | |||||||||
Keywords: | Biomedical Engineering, 0903 Biomedical Engineering, 0906 Electrical And Electronic Engineering, 0801 Artificial Intelligence And Image Processing | |||||||||
Journal or Publication Title: | IEEE Trans Biomed Eng | |||||||||
ISSN: | 1558-2531 | |||||||||
Language: | eng | |||||||||
Publisher License: | Creative Commons: Attribution 3.0 | |||||||||
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PubMed ID: | 29993465 | |||||||||
Go to PubMed abstract | ||||||||||
URI: | https://openaccess.sgul.ac.uk/id/eprint/109359 | |||||||||
Publisher's version: | https://doi.org/10.1109/TBME.2017.2780982 |
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