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Enregistrement W4391648307 · doi:10.2196/50968

Usability and Feasibility Evaluation of a Web-Based and Offline Cybersecurity Resource for Health Care Organizations (The Essentials of Cybersecurity in Health Care Organizations Framework Resource): Mixed Methods Study

2024· article· en· W4391648307 sur OpenAlexvenueno aff
Niki O’Brien, Roberto Fernández Crespo, Fiona O’Driscoll, Mabel Prendergast, Deeph Chana, Ara Darzi, Saira Ghafur

Notice bibliographique

RevueJMIR Formative Research · 2024
Typearticle
Langueen
DomaineComputer Science
ThématiqueInformation and Cyber Security
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute for Health and Care ResearchQatar Foundation
Mots-clésUsabilityHealth careComputer securityResource (disambiguation)Web applicationComputer scienceKnowledge managementWorld Wide WebPolitical science

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Cybersecurity is a growing challenge for health systems worldwide as the rapid adoption of digital technologies has led to increased cyber vulnerabilities with implications for patients and health providers. It is critical to develop workforce awareness and training as part of a safety culture and continuous improvement within health care organizations. However, there are limited open-access, health care-specific resources to help organizations at different levels of maturity develop their cybersecurity practices. OBJECTIVE: This study aims to assess the usability and feasibility of the Essentials of Cybersecurity in Health Care Organizations (ECHO) framework resource and evaluate the strengths, weaknesses, opportunities, and threats associated with implementing the resource at the organizational level. METHODS: A mixed methods, cross-sectional study of the acceptability and usability of the ECHO framework resource was undertaken. The research model was developed based on the technology acceptance model. Members of the Imperial College Leading Health Systems Network and other health care organizations identified through the research teams' networks were invited to participate. Study data were collected through web-based surveys 1 month and 3 months from the date the ECHO framework resource was received by the participants. Quantitative data were analyzed using R software (version 4.2.1). Descriptive statistics were calculated using the mean and 95% CIs. To determine significant differences between the distribution of answers by comparing results from the 2 survey time points, 2-tailed t tests were used. Qualitative data were analyzed using Microsoft Excel. Thematic analysis used deductive and inductive approaches to capture themes and concepts. RESULTS: A total of 16 health care organizations participated in the study. The ECHO framework resource was well accepted and useful for health care organizations, improving their understanding of cybersecurity as a priority area, reducing threats, and enabling organizational planning. Although not all participants were able to implement the resource as part of information computing technology (ICT) cybersecurity activities, those who did were positive about the process of change. Learnings from the implementation process included the usefulness of the resource for raising awareness and ease of use based on familiarity with other standards, guidelines, and tools. Participants noted that several sections of the framework were difficult to operationalize due to costs or budget constraints, human resource limitations, leadership support, stakeholder engagement, and limited time. CONCLUSIONS: The research identified the acceptability and usability of the ECHO framework resource as a health-focused cybersecurity resource for health care organizations. As cybersecurity in health care organizations is everyone's responsibility, there is potential for the framework resource to be used by staff with varied job roles. Future research needs to explore how it can be updated for ICT staff and implemented in practice and how educational materials on different aspects of the framework could be developed.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,020
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,492
Score d'incertitude au seuil0,748

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0200,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,005
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,051
Tête enseignante GPT0,479
Écart entre enseignants0,427 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2024
Routes d'admission1
Résumé présentoui

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