Special Section Papers

Minimizing Ionizing Radiation Exposure in Invasive Cardiology Safety Training for Medical Doctors

[+] Author and Article Information
Aric Katz

Faculty of Industrial Engineering and
Technion—Israel Institute of Technology,
Haifa 3200003, Israel
e-mail: aric@katsoft.co.il

Avraham Shtub

Faculty of Industrial Engineering and
Technion—Israel Institute of Technology,
Haifa 3200003, Israel
e-mail: shtub@ie.technion.ac.il

Ariel Roguin

Interventional Cardiology,
Rambam Medical Center, and
Ruth and Bruce Rappaport Faculty of Medicine,
Technion—Israel Institute of Technology,
Haifa 31096, Israel
e-mail: aroguin@technion.ac.il

1Corresponding author.

Manuscript received June 10, 2016; final manuscript received March 24, 2017; published online May 25, 2017. Assoc. Editor: Jean Koch.

ASME J of Nuclear Rad Sci 3(3), 030905 (May 25, 2017) (8 pages) Paper No: NERS-16-1055; doi: 10.1115/1.4036431 History: Received June 10, 2016; Revised March 24, 2017

Advanced imaging systems, such as C-Arm machines, greatly improve physicians' diagnostic abilities and provide greater precision. Yet, these benefits come with a price of ionizing radiation exposure to medical teams and patients. Supplying proper training and skill improvement to operators on how to use this technology safely can help minimize risk of exposure. Previous studies on radiation knowledge among physicians and radiologists presented disturbing results of underestimated risk of exposure. The following research is based on an innovation in simulation-based training (SBT), a simulator using the Wizard of Oz (WOZ) concept that incorporates an online human trainer and was used for training emergency room (ER) physicians and ultrasound medical personnel. This research integrated WOZ technology with a radiation exposure formula for training to minimize unnecessary radiation exposure. The exposure formula presents real-time and overall exposure levels to operators based on their technique. The simulator also incorporates 3D animation graphics, enabling trainees to simulate the control of various factors. Image quality and the operator's radiation exposure levels are also animated, assisting trainees to focus on their exposure based on their device settings. Contrary to most previous studies, we measured radiation dose to the operator and quantified image quality accordingly. Validation was done on different C-Arm machines. Validation of learning outcomes was done using knowledge exams. Results from our knowledge exams presented significant improvement. The average result of knowledge exams given prior to training was 54%, whereas the average result after training was 94% (p < 0.001). Additionally, after a gap of 2–3 months, high retention was also found.

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Fig. 1

Ebbinghaus forgetting curve

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Fig. 2

User interface as viewed by trainee

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Fig. 3

Overall radiation dose as a factor of image quality (1 = low quality, 5 = high quality)

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Fig. 4

Example of image quality differences

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Fig. 5

(a) Comparison between two zoom points under different angulations, (b) collimation used while zooming, and (c) radiation levels for operator and patient as a factor of angulation, zoom, pulse-rate, shutter, and filter

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Fig. 6

Factors' effect in the prediction model

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Fig. 7

Matched pairs analysis of score by time for knowledge exams

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Fig. 8

Matched pairs of score by time for satisfactory survey



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