메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술저널
저자정보
Shi-Joon Yoo (University of Toronto) Nabil Hussein (Castle Hill Hospital) David J. Barron (University of Toronto)
저널정보
대한의학회 Journal of Korean Medical Science Journal of Korean Medical Science Vol.37 No.38
발행연도
2022.10
수록면
1 - 16 (16page)
DOI
10.3346/jkms.2022.37.e293

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
Congenital heart surgery (CHS) is technically demanding, and its training is extremely complex and challenging. Training of the surgeon’s technical skills has relied on a preceptorship format in which the trainees are gradually exposed to patients in the operating room under the close tutelage of senior staff surgeons. Training in the operating room is an inefficient process and the concept of a learning curve is no longer acceptable in terms of patient outcomes. The benefits of surgical simulation in training of congenital heart surgeons are well known and appreciated. However, adequate surgical simulation models and equipment for training have been scarce until the recent development of three-dimensionally (3D) printed models. Using comprehensive 3D printing and silicone-molding techniques, realistic simulation training models for most congenital heart surgical procedures have been produced. Newly developed silicone-molded models allow efficient CHS training in a stressfree environment with instantaneous feedback from the proctors and avoids risk to patients. The time has arrived when all congenital heart surgeons should consider surgical simulation training before progressing to real-life operating in a similar fashion to the aviation industry where all pilots are required to complete simulation training before flying a real aircraft. It is argued here that simulation training is not an option anymore but should be a mandatory component of CHS training.

목차

등록된 정보가 없습니다.

참고문헌 (0)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0