School of Science, Edith Cowan University, Joondalup, Australia.
JMIR Hum Factors. 2023 Oct 4;10:e48220. doi: 10.2196/48220.
Previous studies have identified that the effective management of cyber security in large health care environments is likely to be significantly impacted by human and social factors, as well as by technical controls. However, there have been limited attempts to confirm this by using measured and integrated studies to identify specific user motivations and behaviors that can be managed to achieve improved outcomes.
This study aims to document and analyze survey and interview data from a diverse range of health care staff members, to determine the primary motivations and behaviors that influence their acceptance and application of cyber security messaging and controls. By identifying these issues, recommendations can be made to positively influence future cyber security governance in health care.
An explanatory sequential mixed methods approach was undertaken to analyze quantitative data from a web-based staff survey (N=103), with a concurrent qualitative investigation applied to data gathered via in-depth staff interviews (N=9). Data from both stages of this methodology were mapped to descriptive variables based on a modified version of the Technology Acceptance Model (TAM; TAM2). After normalization, the quantitative data were verified and analyzed using descriptive statistics, distribution and linearity measures, and a bivariate correlation of the TAM variables to identify the Pearson coefficient (r) and significance (P) values. Finally, after confirming Cronbach α, the determinant score for multicollinearity, and the Kaiser-Meyer-Olkin measure, and applying the Bartlett test of sphericity (χ), an exploratory factor analysis (EFA) was conducted to identify the primary factors with an eigenvalue (λ) >1.0. Comments captured during the qualitative interviews were coded using NVivo software (QSR International) to create an emic-to-etic understanding, which was subsequently integrated with the quantitative results to produce verified conclusions.
Using the explanatory sequential methodology, this study showed that the perceived usefulness of security controls emerged as the most significant factor influencing staff beliefs and behaviors. This variable represented 24% of all the variances measured in the EFA and was also the most common category identified across all coded interviews (281/692, 40.6%). The word frequency analysis showed that systems, patients, and people represented the top 3 recurring themes reported by the interviewees.
To improve cyber security governance in large health care environments, efforts should be focused on demonstrating how confidentiality, integrity, availability, policies, and cloud or vendor-based controls (the main contributors of usefulness measured by the EFA) can directly improve outcomes for systems, staff, and patients. Further consideration also needs to be given to how clinicians should share data and collaborate on patient care, with tools and processes provided to support and manage data sharing securely and to achieve a consistent baseline of secure and normalized behaviors.
先前的研究已经表明,在大型医疗保健环境中,对网络安全的有效管理可能会受到人为因素和社会因素以及技术控制的显著影响。然而,很少有尝试通过使用经过测量和整合的研究来确认这一点,以确定可以管理的特定用户动机和行为,从而实现更好的结果。
本研究旨在记录和分析来自不同医疗保健工作人员的调查和访谈数据,以确定影响他们接受和应用网络安全信息和控制的主要动机和行为。通过确定这些问题,可以提出建议,以积极影响医疗保健领域未来的网络安全治理。
采用解释性顺序混合方法分析了基于网络的员工调查(N=103)的定量数据,并对通过深入员工访谈(N=9)收集的数据进行了同期定性研究。该方法的两个阶段的数据都根据修改后的技术接受模型(TAM;TAM2)基于描述性变量进行了映射。在归一化后,使用描述性统计、分布和线性度度量以及 TAM 变量的双变量相关性来验证和分析定量数据,以确定皮尔逊系数(r)和显着性(P)值。最后,在确认克朗巴赫α、多线性决定分数和凯泽-迈尔-奥尔金度量以及应用巴特利特球形检验(χ)后,进行探索性因素分析(EFA)以确定具有特征值(λ)>1.0 的主要因素。使用 NVivo 软件(QSR International)对定性访谈中捕获的评论进行编码,以创建从内在到外在的理解,然后将其与定量结果相结合,得出经过验证的结论。
使用解释性顺序方法,本研究表明,安全控制的感知有用性是影响员工信念和行为的最重要因素。该变量代表 EFA 中测量的所有方差的 24%,也是所有编码访谈中最常见的类别(281/692,40.6%)。词频分析显示,系统、患者和人员是受访者报告的前 3 个重复主题。
为了改善大型医疗保健环境中的网络安全治理,应集中精力展示机密性、完整性、可用性、政策以及基于云或供应商的控制(EFA 衡量的有用性的主要贡献者)如何直接改善系统、员工和患者的结果。还需要进一步考虑临床医生应该如何共享数据并协作进行患者护理,提供工具和流程以支持和管理安全的数据共享,并实现安全和规范化行为的一致基线。