Unpacking Sociotechnical Discourses on Telehealth Use and Data Protection: A Path Towards Digital Health Value Creation.

BACKGROUND: Telehealth uptake will remain sub-optimal without consumer trust. Safeguarding the security and privacy of health information plays an important role in building trust and acceptance of telehealth. OBJECTIVES: This study seeks to unpack the sociotechnical discourses on the use of telehealth with a focus on privacy and security in the context of United States health services. METHODS: A search of the media outlets facilitated via the Factiva database was conducted. Using a qualitative method, thematic analysis was performed on the news texts to identify the key themes and provide contextual explanations. RESULTS: The analysis led to the identification of three key themes: 'data protection practice', 'clinical resilience', and 'digital health business value' perspectives. These themes focus on various concepts of telehealth use including data privacy, security, public health emergency, compliance activities in the use of telehealth, meeting stakeholders' needs, reducing costs of service delivery, the potential of telehealth for informed action, and improving users' experience. Among these themes, 'data protection practice' was directly associated with privacy compliance and telehealth use. Other thematic discourses have provided an indirect reflection on the role of privacy compliance, with a greater emphasis placed on health service delivery and market dynamics rather than compliance in practice. CONCLUSION: Our study revealed the importance of the COVID-19 pandemic in telehealth use, highlighting the move towards 'good faith' and responsible use of telehealth.

Exploring public opinion about telehealth during COVID-19 by social media analytics.

While COVID-19 catalyzed the acceptance and use of telehealth, our understanding of how it is perceived by multi-stakeholders such as patients, clinicians, and health authorities is limited. Drawing on social media analytics, this research examines social media discourses and users' opinions about telehealth during the COVID-19 pandemic. It applies natural language processing and deep learning to explore word of mouth on telehealth with a contextualized focus on the COVID-19 pandemic. We conducted topic modeling, sentiment analysis, and emotion analysis (fearful, happy, sad, surprised, and angry emotions). The topic modeling analysis led to the identification of 18 topics, representing 6 themes of digital health service delivery, pandemic response, communication and promotion, government action, health service domains (e.g. mental health, cancer, aged care), as well as pharma and drug. The sentiment analysis revealed that while most opinions expressed in tweets were positive, the public expressed mostly negative opinions about certain aspects of COVID-19 such as lockdowns and cyberattacks. Emotion analysis of tweets showed a dominant pattern of fearful and sad emotions in particular topics. The results of this study that inductively emerged from our social media analysis can aid public health authorities and health professionals to address the concerns of telehealth users and improve their experiences.

Data privacy concerns and use of telehealth in the aged care context: An integrative review and research agenda.

OBJECTIVE: Effective use of telehealth offers substantial benefits to older persons and aged care providers. However, data privacy concerns challenge the effective use of telehealth and subsequent business value. Through developing a theoretical model, we explain how privacy concerns can influence the adoption ad use of telehealth in this complex context. METHOD: An integrative review of empirical investigations was conducted by linking privacy concerns, telehealth use, and aged care. We searched three major databases (PubMed, Web of Science, and Scopus) for articles published until December 2020. Articles were analyzed and presented using an integrative theoretical model that we labeled CPCPO (Context-Privacy Concerns-Practice-Outcomes). RESULTS: Our review revealed that privacy concerns are a contextual concept, i.e., different contexts (users, telehealth systems, aged care services, data) produce different privacy concerns. We found that privacy concerns were more voiced in home telecare and were associated with the degree of telemonitoring and surveillance. Contextual privacy concerns were related to video recording, behavioral data (e.g., sleep patterns and eating behavior), location data, and future use of data. These concerns can influence the adoption and use of telehealth. However, privacy protection practices (e.g., informed consent) can help in reducing the concerns and improving the acceptance of telehealth for older persons. CONCLUSION: CPCPO offers contextual explanations of telehealth privacy concerns and systems use. For improving telehealth acceptance, privacy concerns of data processing (e.g., recording, collection, storage, and secondary use) must be addressed by performing data protection practices. Based on the review results, we suggest avenues for future research.

Infodemic, Misinformation and Disinformation in Pandemics: Scientific Landscape and the Road Ahead for Public Health Informatics Research.

In response to epidemics and pandemics, access to authentic sources of information plays a critical role in informing public health practices. However, infodemic, i.e., an overabundance of health information, misinformation, and disinformation, impede implementing best public health policies during a public health crisis such as COVID-19. In this bibliometric study, we aim to report on concept mapping of infodemic literature, and in line with the World Health Organization (WHO)'s repeated calls for actions in managing infodemic, we highlight fruitful avenues for future directions. Through a visualization approach on a set of 414 records, a concept mapping was carried out. This map revealed 42 infodemic-related nodes in five clusters. We also propose an infodemic research platform in which a combination of the research nodes (e.g., COVID, pandemic, disinformation, fake news, post-truth, fact-checking, social networks, Facebook, WhatsApp, and lockdown) with impactful questions suggest future directions.

Health Data Privacy in the COVID-19 Pandemic Context: Discourses on HIPAA.

BACKGROUND: Considering the impacts of the COVID-19 pandemic on health service delivery, the US Office for Civil Rights (OCR) updated the policies on health data processing, and Health Insurance Portability and Accountability Act (HIPAA). OBJECTIVES: In this study, we investigated discourses on HIPAA in relation to COVID-19. METHODS: Through a search of media sources in the Factiva database, relevant texts were identified. We applied a text mining approach to identify concepts and themes in these texts. RESULTS: Our analysis revealed six central themes, namely, Health, HIPAA, Privacy, Security, Patients, and Need, as well as their associated concepts. Among these, Health was the most frequently discussed theme. It comprised concepts such as public, care, emergency, providers, telehealth, entity, use, discretion, OCR, Health and Human Services (HHS), enforcement, business, and services. CONCLUSION: Our discourse analysis of media outlets highlights the role of health data privacy law in the response to global public health emergencies and demonstrates how discourse analysis and computational methods can inform health data protection policymaking in the digital health era.

Profiling adopters (and non-adopters) of a contact tracing mobile application: Insights from Australia.

OBJECTIVE: Many governments are using contact tracing mobile applications (CTMAs) yet public adoption of such systems has been relatively low. The main objective of this paper is to profile adopters (and non-adopters) of Australia's COVIDSafe CTMA. MATERIALS AND METHODS: We use latent profile analysis to examine predictors of CTMA download behaviour. Specifically, we draw on a representative Australian sample (N = 2575) to examine the interplay between age, education, income, dispositional desire for privacy and political ideology on download behaviour. We examine trust in government as a mediating mechanism between profiles and download behaviour. RESULTS: Our analysis produces seven profiles. Trust in government mediates the relationship between most profiles and download behaviour. A combination of wealth and education appear to be key explanatory factors of CTMA download behaviour. Two profiles -- comprising individuals with high income and education -- had the highest rates of download behaviour. Profiles with low download percentages comprised politically left-leaning participants with average to low income and education. CONCLUSION: Our findings clearly indicate the profiles of people who are (not) likely to download a CTMA. Practical ways to improve widespread adoption include providing structural support to the more vulnerable members of society, making clear the societal benefits of downloading CTMAs, and engaging in bipartisan promotion of such apps.

Individuals' privacy concerns and adoption of contact tracing mobile applications in a pandemic: A situational privacy calculus perspective.

OBJECTIVE: The study sought to develop and empirically validate an integrative situational privacy calculus model for explaining potential users' privacy concerns and intention to install a contact tracing mobile application (CTMA). MATERIALS AND METHODS: A survey instrument was developed based on the extant literature in 2 research streams of technology adoption and privacy calculus. Survey participants (N = 853) were recruited from all 50 U.S. states. Partial least squares structural equation modeling was used to validate and test the model. RESULTS: Individuals' intention to install a CTMA is influenced by their risk beliefs, perceived individual and societal benefits to public health, privacy concerns, privacy protection initiatives (legal and technical protection), and technology features (anonymity and use of less sensitive data). We found only indirect relationships between trust in public health authorities and intention to install CTMA. Sex, education, media exposure, and past invasion of privacy did not have a significant relationship either, but interestingly, older people were slightly more inclined than younger people to install a CTMA. DISCUSSION: Our survey results confirm the initial concerns about the potentially low adoption rates of CTMA. Our model provides public health agencies with a validated list of factors influencing individuals' privacy concerns and beliefs, enabling them to systematically take actions to address these identified issues, and increase CTMA adoption. CONCLUSIONS: Developing CTMAs and increasing their adoption is an ongoing challenge for public health systems and policymakers. This research provides an evidence-based and situation-specific model for a better understanding of this theoretically and pragmatically important phenomenon.

Health Data Privacy: Research Fronts, Hot Topics and Future Directions.

Health data privacy is an important research stream due to the high impacts on the success of digital health transformation and implementation. Neglecting to safeguard data confidentially and integrity and mitigate risks associated with unauthorized access will lead to failures in materializing benefit from digital health. This study aims to present a bibliometric analysis of health data privacy and provide a platform for future directions. We conducted a literature search between 2010 and 2020 in the Web of Science (WoS) database, resulted in 1,752 records. As part of the bibliometric analysis, concept mapping of health data privacy researches was depicted by network visualization and overlay visualization. These two visualizations represent five research fronts and emerging topics (e.g., digital health, blockchain, the internet of things (IoT)). Finally, we chart directions for future research on health data privacy, highlighting emerging topics, and boundary-breaking alternatives (e.g., GDPR, contact tracing apps in the context of pandemics).

Towards a contextual theory of Mobile Health Data Protection (MHDP): A realist perspective.

BACKGROUND: With the introduction of privacy regulations such as the California Consumer Privacy Act and the European Union General Data Protection Regulation (GDPR), effective data protection in mobile health (mHealth) is rapidly becoming a concern. However, we have a limited understanding of the contexts and mechanisms that affect the likelihood of failures and successes in mHealth data protection, and their subsequent impacts. In this review and theory development paper, we aim to address this critical knowledge gap. METHOD: We conducted a systematic literature search using PubMed, Embase, and Scopus databases. To synthesize the evidence, we adopted a realist review approach and compiled the extracted information based on context-mechanism-outcome (CMO) configurations. Out of an initial set of 611 records, 19 articles met the eligibility criteria and were included. RESULTS: Our findings indicate that the failures and successes in data protection and their impacts (effective mHealth interventions, data protection awareness, and adoption/use of mHealth systems) depend contingently upon a number of contextual factors (systems, users, tasks, services, geographic elements) and causal mechanisms (unauthorized access, device theft, loss, and sharing, lack of cyber-hygiene, and data protection concerns for failures, and trust building activity, secure and law compliant platforms, and perceived data protection, for successes). We conceptualized the CMO configurations to provide explanations for the reported failures and successes in data protection. CONCLUSION: For effective mHealth interventions, the dark side of system use (data breaches) must be mitigated and remediated. Our study offers a theoretical model that contextually explains how the mechanisms of success and failures work in mHealth.

General Data Protection Regulation (GDPR) in Healthcare: Hot Topics and Research Fronts.

General Data Protection Regulation came into effect across the European Union in May 2018 but its implications in healthcare are yet to be fully understood. The aim of this study was to identify the fronts and hot topics in research on GDPR in healthcare. We analyzed the relevant records in Scopus through bibliometric and scientometric approach and visualization techniques. A set of 155 records was obtained and processed for co-occurrence analysis of key terms and concept mapping. The number of published papers showed a steep rise in the past two years, mainly by European countries. Analysis of the abstract of the papers showed that data protection, privacy, and big data were the most frequently used terms. Three dominant research fronts of GDPR are 1) general implications of GDPR, 2) technology aspects of GDPR, and 3) GDPR in healthcare service. Blockchain and machine learning are among the remerging topics of GDPR research.

Coevolution in management fashion: an agent-based model of consultant-driven innovation.

The rise of management consultancy has been accompanied by increasingly marked faddish cycles in management techniques, but the mechanisms that underlie this relationship are not well understood. The authors develop a simple agent-based framework that models innovation adoption and abandonment on both the supply and demand sides. In opposition to conceptions of consultants as rhetorical wizards who engineer waves of management fashion, firms and consultants are treated as boundedly rational actors who chase the secrets of success by mimicking their highest-performing peers. Computational experiments demonstrate that consultant-driven versions of this dynamic in which the outcomes of firms are strongly conditioned by their choice of consultant are robustly faddish. The invasion of boom markets by low-quality consultants undercuts popular innovations while simultaneously restarting the fashion cycle by prompting the flight of high-quality consultants into less densely occupied niches. Computational experiments also indicate conditions involving consultant mobility, aspiration levels, mimic probabilities, and client-provider matching that attenuate faddishness.

Health information technologies in geriatrics and gerontology: a mixed systematic review.

OBJECTIVE: To review, categorize, and synthesize findings from the literature about the application of health information technologies in geriatrics and gerontology (GGHIT). MATERIALS AND METHODS: This mixed-method systematic review is based on a comprehensive search of Medline, Embase, PsychInfo and ABI/Inform Global. Study selection and coding were performed independently by two researchers and were followed by a narrative synthesis. To move beyond a simple description of the technologies, we employed and adapted the diffusion of innovation theory (DOI). RESULTS: 112 papers were included. Analysis revealed five main types of GGHIT: (1) telecare technologies (representing half of the studies); (2) electronic health records; (3) decision support systems; (4) web-based packages for patients and/or family caregivers; and (5) assistive information technologies. On aggregate, the most consistent finding proves to be the positive outcomes of GGHIT in terms of clinical processes. Although less frequently studied, positive impacts were found on patients' health, productivity, efficiency and costs, clinicians' satisfaction, patients' satisfaction and patients' empowerment. DISCUSSION: Further efforts should focus on improving the characteristics of such technologies in terms of compatibility and simplicity. Implementation strategies also should be improved as trialability and observability are insufficient. CONCLUSIONS: Our results will help organizations in making decisions regarding the choice, planning and diffusion of GGHIT implemented for the care of older adults.