Literature DB >> 30553562

Clinical Cybersecurity Training Through Novel High-Fidelity Simulations.

Christian J Dameff1, Jordan A Selzer2, Jonathan Fisher2, James P Killeen1, Jeffrey L Tully3.   

Abstract

BACKGROUND: Cybersecurity risks in health care systems have traditionally been measured in data breaches of protected health information, but compromised medical devices and critical medical infrastructure present risks of disruptions to patient care. The ubiquitous prevalence of connected medical devices and systems may be associated with an increase in these risks.
OBJECTIVE: This article details the development and execution of three novel high-fidelity clinical simulations designed to teach clinicians to recognize, treat, and prevent patient harm from vulnerable medical devices.
METHODS: Clinical simulations were developed that incorporated patient-care scenarios featuring hacked medical devices based on previously researched security vulnerabilities.
RESULTS: Clinicians did not recognize the etiology of simulated patient pathology as being the result of a compromised device.
CONCLUSIONS: Simulation can be a useful tool in educating clinicians in this new, critically important patient-safety space.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  cybersecurity; health care; medical education; simulation

Mesh:

Year:  2018        PMID: 30553562     DOI: 10.1016/j.jemermed.2018.10.029

Source DB:  PubMed          Journal:  J Emerg Med        ISSN: 0736-4679            Impact factor:   1.484


  3 in total

1.  The First Recall of a Diabetes Device Because of Cybersecurity Risks.

Authors:  David Klonoff; Julia Han
Journal:  J Diabetes Sci Technol       Date:  2019-07-17

2.  Cyber-attacks are a permanent and substantial threat to health systems: Education must reflect that.

Authors:  O'Brien Niki; Ghafur Saira; Sivaramakrishnan Arvind; Durkin Mike
Journal:  Digit Health       Date:  2022-06-16

3.  Behavioral responses to a cyber attack in a hospital environment.

Authors:  Markus Willing; Christian Dresen; Eva Gerlitz; Maximilian Haering; Matthew Smith; Carmen Binnewies; Tim Guess; Uwe Haverkamp; Sebastian Schinzel
Journal:  Sci Rep       Date:  2021-09-29       Impact factor: 4.379

  3 in total

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