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Trends in ransomware attacks on US hospitals, clinics, and other health care delivery organizations, 2016-2021.
Open accessAbstract
Importance: Anecdotal evidence suggests that health care delivery organizations face a growing threat from ransomware attacks that are designed to disrupt care delivery and may consequently threaten patient outcomes. Objective: To quantify the frequency and characteristics of ransomware attacks on health care delivery organizations. Design, Setting, and Participants: This cohort study used data from the Tracking Healthcare Ransomware Events and Traits database to examine the number and characteristics of ransomware attacks on health care delivery organizations from 2016 to 2021. Logistic and negative binomial regression quantified changes over time in the characteristics of ransomware attacks that affected health care delivery organizations. Main Outcomes and Measures: Date of ransomware attack, public reporting of ransomware attacks, personal health information (PHI) exposure, status of encrypted/stolen data following the attack, type of health care delivery organization affected, and operational disruption during the ransomware attack. Results: From January 2016 to December 2021, 374 ransomware attacks on US health care delivery organizations exposed the PHI of nearly 42 million patients. From 2016 to 2021, the annual number of ransomware attacks more than doubled from 43 to 91. Almost half (166 [44.4%]) of ransomware attacks disrupted the delivery of health care, with common disruptions including electronic system downtime (156 [41.7%]), cancellations of scheduled care (38 [10.2%]), and ambulance diversion (16 [4.3%]). From 2016 to 2021, ransomware attacks on health care delivery organizations increasingly affected large organizations with multiple facilities (annual marginal effect [ME], 0.08; 95% CI, 0.05-0.10; P < .001), exposed the PHI of more patients (ME, 66 385.8; 95% CI, 3400.5-129 371.2; P = .04), were less likely to be restored from data backups (ME, -0.04; 95% CI, -0.06 to -0.01; P = .002), were more likely to exceed mandatory reporting timelines (ME, 0.06; 95% CI, 0.03-0.08; P < .001), and increasingly were associated with delays or cancellations of scheduled care (ME, 0.02; 95% CI, 0-0.05; P = .02). Conclusions and Relevance: This cohort study of ransomware attacks documented growth in their frequency and sophistication. Ransomware attacks disrupt care delivery and jeopardize information integrity. Current monitoring/reporting efforts provide limited information and could be expanded to potentially yield a more complete view of how this growing form of cybercrime affects the delivery of health care.
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“Ransomware cyberattacks on healthcare delivery organizations (HDOs) are increasing in frequency and sophistication (1).”
From Ransomware Cyberattack Associated With Cardiac Arrest Incidence and Outcomes at Untargeted, Adjacent Hospitals (2024) · cites this paper as [1]
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Ross, J. M. (2025) · Springer eBooks · doi:10.1007/978-3-031-85546-7_16 · Cited by 1
Predicting Ransomware Incidents with Time-Series Modeling
Roumani, Y., & Roumani, Y. F. (2025) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp5030061 · Cited by 1
“In the healthcare and public health sector, however, the trend is rising, consistent with previous studies [1].”
Leveraging Large Language Models for Malware Detection Through PE Header and Library Analysis
Tane, M. M., Wijaya, N. D., & Hidayaturrahman, H. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.09.046 · Cited by 2
A survey of cyber threat intelligence in embedded system driven security for mobile health systems
Ikegwu, A. C., Nnabue, J. C., Kinse, A. S., & Alo, U. R. (2025) · Journal of Cloud Computing Advances Systems and Applications · doi:10.1186/s13677-025-00811-3 · Cited by 1
Cornejo, G. M. (2025) · Health and Technology · doi:10.1007/s12553-025-01036-9 · Cited by 1
AI in patient care: A narrative review of safety promises and pitfalls
Shopijen, E. (2025) · International Journal of Nursing Sciences · doi:10.1016/j.ijnss.2025.12.003 · Cited by 1
Ensemble Learning Strategies for Anomaly-Based Intrusion Detection in IoMT Systems
Yacoubi, M., Moussaoui, O., & Drocourt, C. (2026) · Springer eBooks · doi:10.1007/978-3-032-08784-3_9 · Cited by 1
International Differences in Windows Remote Desktop Hacking: An Analysis of Honeypot Data
Dearden, T. E., & Bergeron, A. (2026) · Social Science Computer Review · doi:10.1177/08944393261416786 · Cited by 1
Khan, S. I., Rahman, M., Hossain, E., Rahman, M. S., Ashik, A. A. M., & Islam, S. (2026) · International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) · doi:10.1109/icmcsi67283.2026.11412435 · Cited by 1
Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks
Malin, B., Yan, C., & Bonomi, L. (2026) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-092724-031932 · Cited by 1
“Moreover, there is a growing sophistication in ransomware attacks that have been committed against healthcare organizations (10).”
The effect of a national healthcare cyberattack on the Irish hip fracture standards
McCarthy, C. J., Hickey, P., O’Daly, B., McGoldrick, N. P., & Quinlan, J. F. (2026) · The Surgeon · doi:10.1016/j.surge.2026.04.012 · Cited by 1
Chen, Y., & Wu, H. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1819684 · Cited by 1
Wang, S. Q. G., Xue, Y., Wang, D., & Li, R. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2668623 · Cited by 1
Choudhuri, B., Prakash, J., Pal, B., & Veenith, T. (2026) · Indian Journal of Critical Care Medicine · doi:10.5005/jp-journals-10071-25203 · Cited by 1
Straw, I., Kumar, D., Kweon, S., Selzer, J., Dameff, C. J., & Tully, J. L. (2026) · Journal of Medical Internet Research · doi:10.2196/88257 · Cited by 1
“The rise in health care cyberattacks has been described extensively in the literature, with Neprash et al [4] revealing that incidents more than doubled between 2016 and 2021.”
The Growing Impact and Reach of JAMA Health Forum
Ayanian, J. Z. (2023) · JAMA Health Forum · doi:10.1001/jamahealthforum.2023.3077 · Cited by 1
Bakiris, E., Papadakis, N., Katsarou, V., & Alampasis, N. (2024) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3652037.3663888 · Cited by 1
Heath, M. L., Silvera, G. A., & Porter, T. H. (2025) · Health Care Management Review · doi:10.1097/hmr.0000000000000436 · Cited by 1
Tarka, M. C., Blankstein, M., & Schottel, P. C. (2023) · Injury · doi:10.1016/j.injury.2023.111013 · Cited by 1
Ciberseguridad, una prioridad de los servicios de farmacia en la era de la inteligencia artificial
Marín, C. M. H., Monte‐Boquet, E., & Andrés, J. L. P. (2024) · Farmacia Hospitalaria · doi:10.1016/j.farma.2024.08.001 · Cited by 1
Marín, C. M. H., Monte‐Boquet, E., & Andrés, J. L. P. (2024) · Farmacia Hospitalaria · doi:10.1016/j.farma.2024.08.009 · Cited by 1
(2023) · JAMA Health Forum · doi:10.1001/jamahealthforum.2023.0001
Procedural, Substantive, and By-Design Approach toward a Third Stage of Bioethics
Ribas, S. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.1001984
A Comparative Assessment of Threat Identification Methods in EHR Systems
Holík, F., Yeng, P. K., & Fauzi, M. A. (2023) · International Conference on Sustainable Information Engineering and Technology (SIET) · doi:10.1145/3626641.3627493
Cyberattaques contre les systèmes d’information de santé au Canada
Harish, V., Ackery, A., Grant, K. L., Jamieson, T., & Mehta, S. (2024) · Canadian Medical Association Journal · doi:10.1503/cmaj.230436-f
“Il est difficile de faire une comparaison exhaustive des con séquences des attaques sur différents territoires, car de nom breuses cyberattaques contre les systèmes d’information de santé ne sont pas signalées 15 .”
BackgroundKüfner, J. J. K., Tahir, S., Bakhsh, S. T., Mohaisen, L., & Danso, S. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3973-8_27
Metadata
- Authors
- Neprash, H. T., McGlave, C. C., Cross, D. A., Virnig, B. A., Puskarich, M. A., Huling, J. D., Rozenshtein, A. Z., & Nikpay, S. S.
- Affiliations
- University of Minnesota (US)
- University of Florida Health (US)
- Corresponding author
- Neprash, Hannah T.
- ORCID
- Venue
- JAMA Health Forum
- Publisher
- American Medical Association (AMA)
- Published
- 2022-12-29
- Volume
- 3
- Issue
- 12
- Pages
- e224873
- ISSN
- 2689-0186 (ISSN-L)
- PMC
- PMC9856685
- OpenAlex
- W4313325293
- Cited by
- 154
- References
- 8
- Citations by year
- 2026: 49 · 2025: 43 · 2024: 43 · 2023: 19
- FWCI
- 25.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JAMA Health Forum (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Thoreau and American Literature
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Physical Sciences › Computer Science › Software › Advanced Malware Detection Techniques
- Keywords
- ransomware, health care delivery organization, hospitals, clinics, operational disruption, mandatory reporting, ransom negotiation, ransom payments, ambulance diversion, patient safety
- MeSH terms
- Delivery of Health Care, Health Facilities, Hospitals, Humans, Organizations, Cohort Studies
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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