Evidence map›Paper›PMID 40802372›Full record

ArticleJMIR human factors2025

A Sociotechnical Approach to Bring-Your-Own-Device Security in Hospitals: Development and Pilot Testing of a Maturity Model Using Mixed Methods Action Research.

Tafheem Ahmad Wani, Antonette Mendoza, Kathleen Gray

Abstract read
In one paragraph

Article in JMIR human factors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Tafheem Ahmad WaniSchool of Psychology and Public Health, Department of Public Health, La Trobe University, Melbourne, Australia.ORCID 0000-0002-9884-0563
Antonette MendozaCentre for Digital Transformation of Health, University of Melbourne, Melbourne, Australia.ORCID 0000-0002-6903-1169
Kathleen GrayCentre for Digital Transformation of Health, University of Melbourne, Melbourne, Australia.ORCID 0000-0003-1616-2170

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBring 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.

objectiveThis 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.

methodsThis 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.

resultsThe 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.

conclusionsThe 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.

Indexed as

Computer SecurityHospitalsHealth Services ResearchHumansModels, OrganizationalOrganizational CulturePilot ProjectsVictoriaAIartificial intelligencebring your own deviceBYODmaturity modelmHealthmobile healthmobile phonepolicy and governanceprivacysecuritysociotechnical security

Identifiers

PMID40802372
PMCPMC12391842

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.