Hospital Bring-Your-Own-Device Security Challenges and Solutions: Systematic Review of Gray Literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: As familiarity with and convenience of using personal devices in hospitals help improve the productivity, efficiency, and workflow of hospital staff, the health care bring-your-own-device (BYOD) market is growing consistently. However, security concerns owing to the lack of control over the personal mobile devices of staff, which may contain sensitive data such as personal health information of patients, make it one of the biggest health care information technology (IT) challenges for hospital administrations. OBJECTIVE: Given that the hospital BYOD security has not been adequately addressed in peer-reviewed literature, the aim of this paper was to identify key security challenges associated with hospital BYOD usage as well as relevant solutions that can cater to the identified issues by reviewing gray literature. Therefore, this research will provide additional practical insights from current BYOD practices. METHODS: A comprehensive gray literature review was conducted, which followed the stepwise guidelines and quality assessment criteria set out by Garousi et al. The searched literature included tier 1 sources such as health care cybersecurity market reports, white papers, guidelines, policies, and frameworks as well as tier 2 sources such as credible and reputed health IT magazines, databases, and news articles. Moreover, a deductive thematic analysis was conducted to organize the findings based on Schlarman's People Policy Technology model, promoting a holistic understanding of hospitals' BYOD security issues and solutions. RESULTS: A total of 51 sources were found to match the designed eligibility criteria. From these studies, several sociotechnical issues were identified. The major challenges identified were the use of devices with insufficient security controls by hospital staff, lack of control or visibility for the management to maintain security requirements, lack of awareness among hospital staff, lack of direction or guidance for BYOD usage, poor user experience, maintenance of legal requirements, shortage of cybersecurity skills, and loss of devices. Although technologies such as mobile device management, unified endpoint management, containerization, and virtual private network allow better BYOD security management in hospitals, policies and people management measures such as strong security culture and staff awareness and training improve staff commitment in protecting hospital data. CONCLUSIONS: The findings suggest that to optimize BYOD security management in hospitals, all 3 dimensions of the security process (people, policy, and technology) need to be given equal emphasis. As the nature of cybersecurity attacks is becoming more complex, all dimensions should work in close alignment with each other. This means that with the modernization of BYOD technology, BYOD strategy, governance, education, and relevant policies and procedures also need to adapt accordingly.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it