Recent reports indicate that over 85% of data breaches are still caused by a human element, of which healthcare is one of the organizations that cyber criminals target. As healthcare IT infrastructure is characterized by a human element, this study comprehensively examined the effect of psycho-socio-cultural and work factors on security behavior in a typical hospital. A quantitative approach was adopted where we collected responses from 212 healthcare staff through an online questionnaire survey. A broad range of constructs was selected from psychological, social, cultural perception, and work factors based on earlier review work. These were related with some security practices to assess the information security (IS) knowledge, attitude and behavior gaps among healthcare staff in a comprehensive way. The study revealed that work emergency (WE) has a positive correlation with IS conscious care behavior (ISCCB) risk. Conscientiousness also had a positive correlation with ISCCB risk, but agreeableness was negatively correlated with information security knowledge (ISK) risk and information security attitude (ISA) risk. Based on these findings, intrinsic and extrinsic motivation methods combined with cutting-edge technologies can be explored to discourage IS risks behaviors while enhancing conscious care security practice.

A phishing attack is one of the less complicated ways to circumvent sophisticated technical security measures. It is often used to exploit psychological (as as well as other) factors of human users to succeed in social engineering attacks including ransomware. Guided by the state-of-the-arts in a phishing simulation study in healthcare and after deeply assessing the ethical dilemmas, an SMS-based phishing simulation was conducted among healthcare workers in Ghana. The study adopted an in-the-wild study approach alongside quantitative and qualitative surveys. From the state-of-the-art studies, the in-the-wild study approach was the most commonly used method as compared to laboratory-based experiments and statistical surveys because its findings are generally reliable and effective. The attack results also showed that 61% of the targeted healthcare staff were susceptible, and some of the healthcare staff were not victims of the attack because they prioritized patient care and were not susceptible to the simulated phishing attack. Through structural equation modelling, the workload was estimated to have a significant effect on self-efficacy risk (r = 0.5, p-value = 0.05) and work emergency predicted a perceived barrier in the reverse direction at a substantial level of r = −0.46, p-value = 0.00. Additionally, Pearson’s correlation showed that the perceived barrier was a predictor of self-reported security behaviour in phishing attacks among healthcare staff. As a result, various suggestions including an extra workload balancing layer of security controls in emergency departments and better security training were suggested to enhance staff’s conscious care behaviour.

Background: Data breaches in health care are on the rise, emphasizing the need for a holistic approach to mitigation efforts. Objective: The purpose of this study was to develop a comprehensive framework for modeling and analyzing health care professionals’ information security practices related to their individual characteristics, such as their psychological, social, and cultural traits. Methods: The study area was a hospital setting under an ongoing project called the Healthcare Security Practice Analysis, Modeling, and Incentivization (HSPAMI) project. A literature review was conducted for relevant theories and information security practices. The theories and security practices were used to develop an ontology and a comprehensive framework consisting of psychological, social, cultural, and demographic variables. Results: In the review, a number of psychological, social, and cultural theories were identified, including the health belief model, protection motivation theory, theory of planned behavior, and social control theory, in addition to some social demographic variables, to form a comprehensive set of health care professionals’ characteristics. Furthermore, an ontology was developed from these theories to systematically organize the concepts. The framework, called the psychosociocultural (PSC) framework, was then developed from the various combined psychological and sociocultural attributes of the ontology. The Human Aspect of Information Security Questionnaire was adopted as a comprehensive tool for gathering staff security practices as mediating variables in the framework. Conclusions: Data breaches occur often in health care today. This frequency has been attributed to the lack of experience of health care professionals in information security, the lack of development of conscious care security practices, and the lack of motivation to incentivize health care professionals. The frequent data breaches in health care threaten the mutual trust between health care professionals and patients, which implicitly impacts the quality of the health care service. The modeling and analysis of health care professionals’ security practices can be conducted with the PSC framework by combining methods of statistical survey, observations, and interviews in relation to PSC variables, such as perceptions (perceived benefits, perceived threats, and perceived barriers) or psychological traits, social factors, cultural factors, and social demographics.

Background: The time lag in detecting disease outbreaks remains a threat to global health security. The advancement of technology has made health-related data and other indicator activities easily accessible for syndromic surveillance of various datasets. At the heart of disease surveillance lies the clustering algorithm, which groups data with similar characteristics (spatial, temporal, or both) to uncover significant disease outbreak. Despite these developments, there is a lack of updated reviews of trends and modelling options in cluster detection algorithms. Objective: Our purpose was to systematically review practically implemented disease surveillance clustering algorithms relating to temporal, spatial, and spatiotemporal clustering mechanisms for their usage and performance efficacies, and to develop an efficient cluster detection mechanism framework. Methods: We conducted a systematic review exploring Google Scholar, ScienceDirect, PubMed, IEEE Xplore, ACM Digital Library, and Scopus. Between January and March 2018, we conducted the literature search for articles published to date in English in peer-reviewed journals. The main eligibility criteria were studies that (1) examined a practically implemented syndromic surveillance system with cluster detection mechanisms, including over-the-counter medication, school and work absenteeism, and disease surveillance relating to the presymptomatic stage; and (2) focused on surveillance of infectious diseases. We identified relevant articles using the title, keywords, and abstracts as a preliminary filter with the inclusion criteria, and then conducted a full-text review of the relevant articles. We then developed a framework for cluster detection mechanisms for various syndromic surveillance systems based on the review. Results: The search identified a total of 5936 articles. Removal of duplicates resulted in 5839 articles. After an initial review of the titles, we excluded 4165 articles, with 1674 remaining. Reading of abstracts and keywords eliminated 1549 further records. An in-depth assessment of the remaining 125 articles resulted in a total of 27 articles for inclusion in the review. The result indicated that various clustering and aberration detection algorithms have been empirically implemented or assessed with real data and tested. Based on the findings of the review, we subsequently developed a framework to include data processing, clustering and aberration detection, visualization, and alerts and alarms. Conclusions: The review identified various algorithms that have been practically implemented and tested. These results might foster the development of effective and efficient cluster detection mechanisms in empirical syndromic surveillance systems relating to a broad spectrum of space, time, or space-time.

Background: The loss of human lives from cyberattacks in health care is no longer a probabilistic quantification but a reality that has begun. In addition, the threat scope is also expanding to involve a threat of national security, among others, resulting in surging data breaches within the health care sector. For that matter, there have been provisions of various legislation, regulations, and information security governance tools such as policies, standards, and directives toward enhancing health care information security–conscious care behavior among users. Meanwhile, in a research scenario, there are no comprehensive required security practices to serve as a yardstick in assessing security practices in health care. Moreover, an analysis of the holistic view of the requirements that need more concentration of management, end users, or both has not been comprehensively developed. Thus, there is a possibility that security practice research will leave out vital requirements. Objective: The objective of this study was to systematically identify, assess, and analyze the state-of-the-art information security requirements in health care. These requirements can be used to develop a framework to serve as a yardstick for measuring the future real security practices of health care staff. Methods: A scoping review was, as a result, adopted to identify, assess, and analyze the information security requirement sources within health care in Norway, Indonesia, and Ghana. Results: Of 188 security and privacy requirement sources that were initially identified, 130 (69.1%) were fully read by the authors. Subsequently, of these 188 requirement documents, 82 (43.6%) fully met the inclusion criteria and were accessed and analyzed. In total, 253 security and privacy requirements were identified in this work. The findings were then used to develop a framework to serve as a benchmark for modeling and analyzing health care security practices. Conclusions: On the basis of these findings, a framework for modeling, analyzing, and developing effective security countermeasures, including incentivization measures, was developed. Following this framework, research results of health care security practices would be more reliable and effective than relying on incomprehensive security requirements.