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.

Large language models and generative AI in telehealth: a responsible use lens.

OBJECTIVE: This scoping review aims to assess the current research landscape of the application and use of large language models (LLMs) and generative Artificial Intelligence (AI), through tools such as ChatGPT in telehealth. Additionally, the review seeks to identify key areas for future research, with a particular focus on AI ethics considerations for responsible use and ensuring trustworthy AI. MATERIALS AND METHODS: Following the scoping review methodological framework, a search strategy was conducted across 6 databases. To structure our review, we employed AI ethics guidelines and principles, constructing a concept matrix for investigating the responsible use of AI in telehealth. Using the concept matrix in our review enabled the identification of gaps in the literature and informed future research directions. RESULTS: Twenty studies were included in the review. Among the included studies, 5 were empirical, and 15 were reviews and perspectives focusing on different telehealth applications and healthcare contexts. Benefit and reliability concepts were frequently discussed in these studies. Privacy, security, and accountability were peripheral themes, with transparency, explainability, human agency, and contestability lacking conceptual or empirical exploration. CONCLUSION: The findings emphasized the potential of LLMs, especially ChatGPT, in telehealth. They provide insights into understanding the use of LLMs, enhancing telehealth services, and taking ethical considerations into account. By proposing three future research directions with a focus on responsible use, this review further contributes to the advancement of this emerging phenomenon of healthcare AI.

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.

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.