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Healthcare Data Ownership and Privacy: A Perspective for Digital Therapeutics
Published in Oleksandr Sverdlov, Joris van Dam, Digital Therapeutics, 2023
Ximena Benavides, Greg Licholai
Further, HIPAA protects only identifiable health information, so its framework does not apply to commercial and research mHealth apps (Rothstein, 2016). Traditionally, the term “health data” referred to information produced and stored by healthcare provider organizations; however, today, vast amounts of “health-relevant data” are actively and passively collected from individuals and entities elsewhere (McGraw and Mandl, 2021). Data that identifies those most in need of support and services could be perversely used to make powerful inferences about individuals' or populations' health resulting in penalties for poor medication adherence or not being insured.
Applications of Health Data
Published in Disa Lee Choun, Anca Petre, Digital Health and Patient Data, 2023
Health data includes any patient’s data captured either digitally or on paper. In order to have the best benefit, all health data needs to be digital format in order to analyze the information for the different applications or use to benefit the patients (the ultimate stakeholder). Many countries have started the painful transition of moving the citizen’s health data to electronic. These technologies that can transmit and receive electronic health data are referred to as digital health. It includes technologies like mobile health (mHealth), platforms, and systems that engage consumers for lifestyle, wellness, health-related purposes, provide real-time monitoring of patient’s vital signs, collect digital social and behavioural information including patient reported data, deliver information to care providers and/or researchers, and/or support life science and clinical operations. It can also directly or indirectly monitor or enhance health or coordinate healthcare services.
Exploring the Scope of Policy Issues Influencing IoT Health and Big Data: A Structured Review
Published in Adarsh Garg, D. P. Goyal, Global Healthcare Disasters, 2023
IoT devices, cloud computing, and machine learning are among the most prominent technologies intertwined with health big data applications (Riazul Islam et al., 2015). The deployment of these technologies has implications for data capture, storage, analytics, and reporting, including quality and accessibility of health information (Auffray et al., 2016). Technology is equally pivotal to ensure security and privacy of health data using authentication, encryption, data masking, access control, and monitoring and auditing techniques (Abouelmehdi et al., 2018).
Challenges in the Ethics and Implementation of Learning Health Care Systems
Published in The American Journal of Bioethics, 2023
Robert M. Califf, Ruth Faden, Nancy Kass, Stephanie Morain, Matthew Crane
In addition, despite the fact that the vast majority of Americans express the desire to have their health data used to advance knowledge, we have collectively failed to develop a convincing paradigm for broad participation and data sharing at the level of the individual person in the context of routine health care delivery. Concerns about privacy and confidentiality continue to dominate public discussions, and the lack of agreement on data sharing even among third parties who have secured access to patient data remains a significant barrier. Clear delineation of the reciprocal obligations of those who benefit from data access is lacking in a manner that not only ensures sufficient privacy and confidentiality but also encourages data sharing in a sufficiently widespread manner to allow a far more expansive ability to secure much needed answers to common clinical questions.
Interrupting HIV transmission networks: how can we design and implement timely and effective interventions?
Published in Expert Review of Anti-infective Therapy, 2023
Ann M. Dennis, Victoria Mobley
Leveraging community-based organizations (CBOs) and local advocacy groups is essential for the success of public health initiatives. Interventions based on HIV-related public health data in particular face challenges such as ongoing societal stigma, misinformation about HIV treatment and prevention, and health system mistrust stemming from historical marginalization among racial minority groups who now experience the highest HIV burden. While name-based reporting and confidential partner notification of new HIV diagnoses have been routine in most states since the early days of the epidemic, the volume of clinical data collected and used for response has expanded. Many PWH and other community members may be unfamiliar with such reporting and those public health programs that use data to help prioritize services for PWH who appear to be out of care. The required reporting of sequences to the NHSS and subsequent routine CDR activities have fueled ongoing debates on the ethical implications on the use of such data by public health systems [15]. Assurances in public health data privacy, security, and confidentiality remain paramount, including how such public health data can be protected from use in control measure violations. Regardless of the extent MHE is used in response activities, ongoing collaborative partnerships with CBOs and other community groups will foster trust and accountability that are essential elements of successful interventions.
Integrating artificial intelligence into an ophthalmologist’s workflow: obstacles and opportunities
Published in Expert Review of Ophthalmology, 2023
Priyal Taribagil, HD Jeffry Hogg, Konstantinos Balaskas, Pearse A Keane
Privacy and data protection issues are not unique to AI-enabled tools, and relevant policy and regulatory frameworks are well-established. Patient health data warrant robust adherence to such laws due to the nature of the data. In Europe, The General Data Protection Regulation (GDPR) is responsible for the data protection laws that are enforced. This applies to the rights of processing ‘personal data’ – which are defined as ‘information related to an identified or identifiable person’ [96]. Although the conceptual framework of the GDPR is not explicitly designed for AI implementation, there are many components that are relevant and currently used in practice. Particularly within the medical context, ‘data concerning health’ are distinctly defined as relatiedto the ‘physical and mental health of a natural person.’ Under the GDPR regulation, health data, genetic data, and biometrics are all included in a special category of sensitive information. Processing of such data is only permissible provided that there is explicit patient consent or where it is required for public or scientific interest and research [99].