A Sociotechnical Approach to Bring-Your-Own-Device Security in Hospitals: Development and Pilot Testing of a Maturity Model Using Mixed Methods Action Research
Bibliographic record
Abstract
BACKGROUND: Bring your own device (BYOD) adoption in health care improves clinician productivity, but introduces cybersecurity risks due to weak security controls, human error, and policy circumvention. Existing security frameworks and models are technocentric, while overlooking sociotechnical factors such as clinician behavior, workflow integration, and organizational culture. This misalignment reduces their effectiveness in health care settings. In addition, hospitals vary in structure, resources, and BYOD use, necessitating a flexible yet structured approach to assess security maturity and prioritize improvements, which is lacking in existing models. OBJECTIVE: This study aims to develop and pilot a hospital BYOD security maturity model that integrates technical, policy, and human factors for a structured assessment and improvement of BYOD security in health care. METHODS: This study used mixed methods action research to design and pilot a hospital BYOD security maturity model. Surveys and interviews with IT managers and clinicians shaped the model, which was trialed at a public metropolitan hospital in Victoria, Australia. Participants completed a maturity assessment and joined a 90‑minute co‑design workshop that prioritized 6 key domains and proposed improvements. Descriptive statistics and thematic analysis guided refinements to improve clarity and usability. RESULTS: The model was initially developed with 22 domains across 3 key dimensions: technology, policy, and people, each structured across 5 maturity levels to support systematic progression in hospital BYOD security. On the basis of participant feedback during the refinement process, 2 training-related domains were merged, resulting in a final model with 21 domains. The technology dimension includes domains such as identity, access, and authentication management; device security; and clinical communication, ensuring technical controls align with hospital policies and workflows. The policy dimension focuses on governance, covering areas such as BYOD strategy, regulatory compliance, and incident response, to establish clear security guidelines and enforcement mechanisms. The people dimension addresses human factors, including security awareness training, stakeholder involvement, and security culture, fostering staff engagement and adherence to security protocols. A maturity assessment survey conducted at a public metropolitan hospital in Victoria, Australia, revealed an overall maturity level of 2.04. Key areas for improvement included identity and access management, clinical communication security, and governance transparency. A 90-minute co-design workshop identified challenges and proposed solutions for the top 6 priority domains. Recommendations included implementing single sign-on, defining a formal BYOD strategy, enhancing secure communication tools, and improving stakeholder engagement. CONCLUSIONS: The model can serve as a valuable tool for hospitals and policy makers, offering actionable recommendations to strengthen BYOD security. The pilot implementation demonstrated its practical applicability, helping the hospital identify security gaps and develop a road map for structured enhancements. Further validation across diverse health care settings will enhance its adaptability and long-term impact.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.110 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".