The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Cybersecurity in Hospitals: A Systematic, Organizational Perspective
Open accessAbstract
BACKGROUND: Cybersecurity incidents are a growing threat to the health care industry in general and hospitals in particular. The health care industry has lagged behind other industries in protecting its main stakeholder (ie, patients), and now hospitals must invest considerable capital and effort in protecting their systems. However, this is easier said than done because hospitals are extraordinarily technology-saturated, complex organizations with high end point complexity, internal politics, and regulatory pressures. OBJECTIVE: The purpose of this study was to develop a systematic and organizational perspective for studying (1) the dynamics of cybersecurity capability development at hospitals and (2) how these internal organizational dynamics interact to form a system of hospital cybersecurity in the United States. METHODS: We conducted interviews with hospital chief information officers, chief information security officers, and health care cybersecurity experts; analyzed the interview data; and developed a system dynamics model that unravels the mechanisms by which hospitals build cybersecurity capabilities. We then use simulation analysis to examine how changes to variables within the model affect the likelihood of cyberattacks across both individual hospitals and a system of hospitals. RESULTS: We discuss several key mechanisms that hospitals use to reduce the likelihood of cybercriminal activity. The variable that most influences the risk of cyberattack in a hospital is end point complexity, followed by internal stakeholder alignment. Although resource availability is important in fueling efforts to close cybersecurity capability gaps, low levels of resources could be compensated for by setting a high target level of cybersecurity. CONCLUSIONS: To enhance cybersecurity capabilities at hospitals, the main focus of chief information officers and chief information security officers should be on reducing end point complexity and improving internal stakeholder alignment. These strategies can solve cybersecurity problems more effectively than blindly pursuing more resources. On a macro level, the cyber vulnerability of a country's hospital infrastructure is affected by the vulnerabilities of all individual hospitals. In this large system, reducing variation in resource availability makes the whole system less vulnerable-a few hospitals with low resources for cybersecurity threaten the entire infrastructure of health care. In other words, hospitals need to move forward together to make the industry less attractive to cybercriminals. Moreover, although compliance is essential, it does not equal security. Hospitals should set their target level of cybersecurity beyond the requirements of current regulations and policies. As of today, policies mostly address data privacy, not data security. Thus, policy makers need to introduce policies that not only raise the target level of cybersecurity capabilities but also reduce the variability in resource availability across the entire health care system.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Qualitative research and evaluation methods
Patton, M. Q. (1980) · Cited by 35,626
Crossing the Quality Chasm: A New Health System for the 21st Century
Bloom, B. S. (2002) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.287.5.646-a · Cited by 3,726
The Internet of Things for Health Care: A Comprehensive Survey
Riazul Islam, S. M., Kwak, D., Humaun Kabir, M., Hossain, M., & Kwak, K.-S. (2015) · IEEE Access · doi:10.1109/access.2015.2437951 · Cited by 3,095
Leavitt, M. (2001) · PubMed · Cited by 1,862
Crossing the Quality Chasm: A New Health System for the 21st Century
(2001) · National Academies Press eBooks · doi:10.17226/10027 · Cited by 770
Data Collection Methods: Semi-Structured Interviews and Focus Groups
(2009) · doi:10.7249/tr718 · Cited by 524
Collecting and analyzing qualitative data for system dynamics: methods and models
Luna‐Reyes, L. F., & Andersen, D. L. (2003) · System Dynamics Review · doi:10.1002/sdr.280 · Cited by 505
Cybersecurity in healthcare: A systematic review of modern threats and trends
Kruse, C. S., Frederick, B., Jacobson, T., & Monticone, D. K. (2017) · Technology and Health Care · doi:10.3233/thc-161263 · Cited by 474
Angst, C. M., Block, E. S., D’Arcy, J., & Kelley, K. (2017) · MIS Quarterly · doi:10.25300/misq/2017/41.3.10 · Cited by 230
A Socio-technical Approach to Preventing, Mitigating, and Recovering from Ransomware Attacks
Singh, H., & Sittig, D. F. (2016) · Applied Clinical Informatics · doi:10.4338/aci-2016-04-soa-0064 · Cited by 109
Cited by
CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning
Mirsky, Y., Mahler, T., Shelef, I., & Elovici, Y. (2019) · arXiv · doi:10.48550/arxiv.1901.03597 · Cited by 168
Medical Image Tampering Detection: A New Dataset and Baseline
Reichman, B., Jing, L., Akin, O., & Tian, Y. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-68763-2_20 · Cited by 13
Metadata
- Authors
- Jalali, M., & Kaiser, J.
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2018
- Volume
- 20
- Issue
- 5
- Pages
- e10059-e10059
- ISSN
- 1438-8871 (ISSN-L)
- 1556-5068
- OpenAlex
- W2788456181
- Cited by
- 265
- References
- 19
- FWCI
- 34.15
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Cybersecurity in Hospitals: A Systematic, Organizational Perspective. https://app.cochat.ai/papers/10.2139/ssrn.3100364
- The paper
- Jalali, M., & Kaiser, J. (2018). Cybersecurity in Hospitals: A Systematic, Organizational Perspective. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3100364
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.