TY - GEN
T1 - Study of Software-Related Causes in the FDA Medical Device Recalls
AU - Fu, Zhicheng
AU - Guo, Chunhui
AU - Ren, Shangping
AU - Jiang, Yu
AU - Sha, Lui
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - As technology advances, medical devices are playing increasingly more important roles in patient care. Unfortunately, based on the U.S. Food and Drug Administration (FDA) data, medical device recalls are at an all time high. One of the major causes of the recalls is due to defective software. In fact, one in every three medical devices that use software for operation has been recalled because of failures in the software itself. Unlike traditional software, software-based medical devices have specific domain fault modes, and these fault modes have been not addressed in software design literature, such as dosage calculation fault. In this paper, we first present a process that collects software-related medical device recalls from the FDA database. Collecting all software-related medical device recalls is an effort that needs the support and contributions from a large research, industrial, and medical community, To facility such effort, we have developed a web-based platform for different users to contribute and share new software-related medical device recalls into the collection. Second, we analyze one hundred software-related recalls that we have collected from the FDA database. Our analysis reveals that there are four major categories of software failures in medical device recalls and implicit assumptions made by medical device manufacturers are among one of the leading causes in medical device recalls. Last, we present an approach for implicit assumption management in medical cyber-physical system designs.
AB - As technology advances, medical devices are playing increasingly more important roles in patient care. Unfortunately, based on the U.S. Food and Drug Administration (FDA) data, medical device recalls are at an all time high. One of the major causes of the recalls is due to defective software. In fact, one in every three medical devices that use software for operation has been recalled because of failures in the software itself. Unlike traditional software, software-based medical devices have specific domain fault modes, and these fault modes have been not addressed in software design literature, such as dosage calculation fault. In this paper, we first present a process that collects software-related medical device recalls from the FDA database. Collecting all software-related medical device recalls is an effort that needs the support and contributions from a large research, industrial, and medical community, To facility such effort, we have developed a web-based platform for different users to contribute and share new software-related medical device recalls into the collection. Second, we analyze one hundred software-related recalls that we have collected from the FDA database. Our analysis reveals that there are four major categories of software failures in medical device recalls and implicit assumptions made by medical device manufacturers are among one of the leading causes in medical device recalls. Last, we present an approach for implicit assumption management in medical cyber-physical system designs.
KW - Implicit assumptions
KW - Medical cyberphysical systems
KW - Medical device recalls analysis
KW - Root causes
KW - Software fault modes
UR - http://www.scopus.com/inward/record.url?scp=85045286162&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045286162&partnerID=8YFLogxK
U2 - 10.1109/ICECCS.2017.20
DO - 10.1109/ICECCS.2017.20
M3 - Conference contribution
AN - SCOPUS:85045286162
T3 - Proceedings of the IEEE International Conference on Engineering of Complex Computer Systems, ICECCS
SP - 60
EP - 69
BT - Proceedings - 2017 22nd International Conference on Engineering of Complex Computer Systems, ICECCS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd International Conference on Engineering of Complex Computer Systems, ICECCS 2017
Y2 - 6 November 2017 through 8 November 2017
ER -