Cyber Hygiene Methodology for Raising Cybersecurity and Data Privacy Awareness in Health Care Organizations: Concept Study.

awareness cyber hygiene cybersecurity health care mobile phone risk management training

Journal

Journal of medical Internet research
ISSN: 1438-8871
Titre abrégé: J Med Internet Res
Pays: Canada
ID NLM: 100959882

Informations de publication

Date de publication:
27 07 2023
Historique:
received: 21 07 2022
accepted: 19 01 2023
revised: 23 12 2022
medline: 28 7 2023
pubmed: 27 7 2023
entrez: 27 7 2023
Statut: epublish

Résumé

Cyber threats are increasing across all business sectors, with health care being a prominent domain. In response to the ever-increasing threats, health care organizations (HOs) are enhancing the technical measures with the use of cybersecurity controls and other advanced solutions for further protection. Despite the need for technical controls, humans are evidently the weakest link in the cybersecurity posture of HOs. This suggests that addressing the human aspects of cybersecurity is a key step toward managing cyber-physical risks. In practice, HOs are required to apply general cybersecurity and data privacy guidelines that focus on human factors. However, there is limited literature on the methodologies and procedures that can assist in successfully mapping these guidelines to specific controls (interventions), including awareness activities and training programs, with a measurable impact on personnel. To this end, tools and structured methodologies for assisting higher management in selecting the minimum number of required controls that will be most effective on the health care workforce are highly desirable. This study aimed to introduce a cyber hygiene (CH) methodology that uses a unique survey-based risk assessment approach for raising the cybersecurity and data privacy awareness of different employee groups in HOs. The main objective was to identify the most effective strategy for managing cybersecurity and data privacy risks and recommend targeted human-centric controls that are tailored to organization-specific needs. The CH methodology relied on a cross-sectional, exploratory survey study followed by a proposed risk-based survey data analysis approach. First, survey data were collected from 4 different employee groups across 3 European HOs, covering 7 categories of cybersecurity and data privacy risks. Next, survey data were transcribed and fitted into a proposed risk-based approach matrix that translated risk levels to strategies for managing the risks. A list of human-centric controls and implementation levels was created. These controls were associated with risk categories, mapped to risk strategies for managing the risks related to all employee groups. Our mapping empowered the computation and subsequent recommendation of subsets of human-centric controls to implement the identified strategy for managing the overall risk of the HOs. An indicative example demonstrated the application of the CH methodology in a simple scenario. Finally, by applying the CH methodology in the health care sector, we obtained results in the form of risk markings; identified strategies to manage the risks; and recommended controls for each of the 3 HOs, each employee group, and each risk category. The proposed CH methodology improves the CH perception and behavior of personnel in the health care sector and provides risk strategies together with a list of recommended human-centric controls for managing a wide range of cybersecurity and data privacy risks related to health care employees.

Sections du résumé

BACKGROUND
Cyber threats are increasing across all business sectors, with health care being a prominent domain. In response to the ever-increasing threats, health care organizations (HOs) are enhancing the technical measures with the use of cybersecurity controls and other advanced solutions for further protection. Despite the need for technical controls, humans are evidently the weakest link in the cybersecurity posture of HOs. This suggests that addressing the human aspects of cybersecurity is a key step toward managing cyber-physical risks. In practice, HOs are required to apply general cybersecurity and data privacy guidelines that focus on human factors. However, there is limited literature on the methodologies and procedures that can assist in successfully mapping these guidelines to specific controls (interventions), including awareness activities and training programs, with a measurable impact on personnel. To this end, tools and structured methodologies for assisting higher management in selecting the minimum number of required controls that will be most effective on the health care workforce are highly desirable.
OBJECTIVE
This study aimed to introduce a cyber hygiene (CH) methodology that uses a unique survey-based risk assessment approach for raising the cybersecurity and data privacy awareness of different employee groups in HOs. The main objective was to identify the most effective strategy for managing cybersecurity and data privacy risks and recommend targeted human-centric controls that are tailored to organization-specific needs.
METHODS
The CH methodology relied on a cross-sectional, exploratory survey study followed by a proposed risk-based survey data analysis approach. First, survey data were collected from 4 different employee groups across 3 European HOs, covering 7 categories of cybersecurity and data privacy risks. Next, survey data were transcribed and fitted into a proposed risk-based approach matrix that translated risk levels to strategies for managing the risks.
RESULTS
A list of human-centric controls and implementation levels was created. These controls were associated with risk categories, mapped to risk strategies for managing the risks related to all employee groups. Our mapping empowered the computation and subsequent recommendation of subsets of human-centric controls to implement the identified strategy for managing the overall risk of the HOs. An indicative example demonstrated the application of the CH methodology in a simple scenario. Finally, by applying the CH methodology in the health care sector, we obtained results in the form of risk markings; identified strategies to manage the risks; and recommended controls for each of the 3 HOs, each employee group, and each risk category.
CONCLUSIONS
The proposed CH methodology improves the CH perception and behavior of personnel in the health care sector and provides risk strategies together with a list of recommended human-centric controls for managing a wide range of cybersecurity and data privacy risks related to health care employees.

Identifiants

pubmed: 37498644
pii: v25i1e41294
doi: 10.2196/41294
pmc: PMC10415935
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e41294

Informations de copyright

©Elina Argyridou, Sokratis Nifakos, Christos Laoudias, Sakshyam Panda, Emmanouil Panaousis, Krishna Chandramouli, Diana Navarro-Llobet, Juan Mora Zamorano, Panagiotis Papachristou, Stefano Bonacina. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 27.07.2023.

Références

Int J Qual Health Care. 2021 Feb 20;33(1):
pubmed: 33351134
JMIR Mhealth Uhealth. 2020 Jun 18;8(6):e18175
pubmed: 32554388
Maturitas. 2018 Jul;113:48-52
pubmed: 29903648
Circulation. 2018 Sep 18;138(12):1274-1276
pubmed: 29748188
J Med Internet Res. 2020 Sep 17;22(9):e23692
pubmed: 32897869
J Med Internet Res. 2018 May 28;20(5):e10059
pubmed: 29807882
Sensors (Basel). 2021 Jul 28;21(15):
pubmed: 34372354
J Med Internet Res. 2020 Jan 23;22(1):e16775
pubmed: 32012071

Auteurs

Elina Argyridou (E)

KIOS Research and Innovation Center of Excellence, University of Cyprus, Nicosia, Cyprus.

Sokratis Nifakos (S)

Health Informatics Centre, Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden.

Christos Laoudias (C)

KIOS Research and Innovation Center of Excellence, University of Cyprus, Nicosia, Cyprus.

Sakshyam Panda (S)

Internet of Things and Security Centre, University of Greenwich, London, United Kingdom.

Emmanouil Panaousis (E)

Internet of Things and Security Centre, University of Greenwich, London, United Kingdom.

Krishna Chandramouli (K)

Health Informatics Centre, Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden.

Diana Navarro-Llobet (D)

Department of Research and Innovation, Fundacio Privada Hospital Asil de Granollers, Barcelona, Spain.

Juan Mora Zamorano (J)

Instituto de Invest, Sanitaria Puerta de Hierro, Servicio Madrileno de Salud , Majadahonda, Madrid, Spain.

Panagiotis Papachristou (P)

Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.

Stefano Bonacina (S)

Health Informatics Centre, Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden.

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