Hospital Bring-Your-Own-Device Security Challenges and Solutions: Systematic Review of Gray Literature
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,022 | 0,076 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,005 |
| Bibliométrie | 0,038 | 0,029 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».