ReviewFrontiers in digital health2022
Hospital cybersecurity risks and gaps: Review (for the non-cyber professional).
Review in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Vulnerability to Cyberattacks and Sociotechnical Solutions for Health Care Systems: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Mapping Machine Learning-Driven Cybersecurity Solutions in Health Care: Scoping Literature Review.Journal of medical Internet research · 2026Article
- Toward equitable digital health: an integrated framework addressing exclusion, ethics, and implementation across healthcare systems.International journal for equity in health · 2026Review
- Workplace telepressure and workplace cognitive failure among hospital financial personnel: a mediation analysis and latent profile analysis based on work-family conflict and self-regulatory fatigue.Frontiers in public health · 2026Article
- Evaluation of an AI-Based Clinical Decision Support System for Perioperative Care of Older Patients: Ethical Analysis of Focus Groups With Older Adults.JMIR aging · 2025Article
- Sociotechnical Cybersecurity Framework for Securing Health Care From Vulnerabilities and Cyberattacks: Scoping Review.Journal of medical Internet research · 2025Article
- Review
- Review
- Improving authenticity and provenance in digital biomarkers: the case for digital watermarking.NPJ digital medicine · 2025Article
- Article
- Design and Pilot Study of a High-Fidelity Medical Simulation of a Hospital-Wide Cybersecurity Attack.Research square · 2024Article
- Review
- Doctors' perception on the ethical use of AI-enabled clinical decision support systems for antibiotic prescribing recommendations in Singapore.Frontiers in public health · 2024Article
- AI illuminates paths in oral cancer: transformative insights, diagnostic precision, and personalized strategies.EXCLI journal · 2024Review
- Article
- Economic Impact of a Hospital Cyberattack in a National Health System: Descriptive Case Study.JMIR formative research · 2023Article
- Review
- Data security crisis in universities: identification of key factors affecting data breach incidents.Humanities & social sciences communications · 2023Article
- Biosafety and biosecurity challenges during the COVID-19 pandemic and beyond.Frontiers in bioengineering and biotechnology · 2023Article
- The Role of Cybersecurity Education in Nigeria's Healthcare Sector.Nigerian medical journal : journal of the Nigeria Medical AssociationReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Healthcare is facing a growing threat of cyberattacks. Myriad data sources illustrate the same trends that healthcare is one of the industries with the highest risk of cyber infiltration and is seeing a surge in security incidents within just a few years. The circumstances thus begged the question: are US hospitals prepared for the risks that accompany clinical medicine in cyberspace? Objective: The study aimed to identify the major topics and concerns present in today's hospital cybersecurity field, intended for non-cyber professionals working in hospital settings. Methods: Results: The review revealed a basic landscape of hospital cybersecurity, including primary reasons hospitals are frequent targets, top attack methods, and consequences hospitals face following attacks. Cyber technologies common in healthcare and their risks were examined, including medical devices, telemedicine software, and electronic data. By infiltrating any of these components of clinical care, attackers can access mounds of information and manipulate, steal, ransom, or otherwise compromise the records, or can use the access to catapult themselves to deeper parts of a hospital's network. Issues that can increase healthcare cyber risks, like interoperability and constant accessibility, were also identified. Finally, strategies that hospitals tend to employ to combat these risks, including technical, financial, and regulatory, were explored and found to be weak. There exist serious vulnerabilities within hospitals' technologies that many hospitals presently fail to address. The COVID-19 pandemic was used to further illustrate this issue. Conclusions: Comparison of the risks, strategies, and gaps revealed that many US hospitals are unprepared for cyberattacks. Efforts are largely misdirected, with external-often governmental-efforts negligible. Policy changes, e.g., training employees in cyber protocols, adding advanced technical protections, and collaborating with several experts, are necessary. Overall, hospitals must recognize that, in cyber incidents, the real victims are the patients. They are at risk physically and digitally when medical devices or treatments are compromised.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.