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Title: Assessing resilience of hospitals to cyberattack
Objective This paper investigates the impact on emergency hospital services from initiation through recovery of a ransomware attack affecting the emergency department, intensive care unit and supporting laboratory services. Recovery strategies of paying ransom to the attackers with follow-on restoration and in-house full system restoration from backup are compared. Methods A multi-unit, patient-based and resource-constrained discrete-event simulation model of a typical U.S. urban tertiary hospital is adapted to model the attack, its impacts, and tested recovery strategies. The model is used to quantify the hospital's resilience to cyberattack. Insights were gleaned from systematically designed numerical experiments. Results While paying the ransom was found to result in some short-term gains assuming the perpetrators actually provide the decryption key as promised, in the longer term, the results of this study suggest that paying the ransom does not pay off. Rather, paying the ransom, when considered at the end of the event when services are fully restored, precluded significantly more patients from receiving critically needed care. Also noted was a lag in recovery for the intensive care unit as compared with the emergency department. Such a lag must be considered in preparedness plans. Conclusion Vulnerability to cyberattacks is a major challenge to the more » healthcare system. This paper provides a methodology for assessing the resilience of a hospital to cyberattacks and analyzing the effects of different response strategies. The model showed that paying the ransom resulted in short-term gains but did not pay off in the longer term. « less
Authors:
; ; ; ;
Award ID(s):
2027624
Publication Date:
NSF-PAR ID:
10324373
Journal Name:
DIGITAL HEALTH
Volume:
7
Page Range or eLocation-ID:
205520762110593
ISSN:
2055-2076
Sponsoring Org:
National Science Foundation
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We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. 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