The use of new technologies such as digital health applications, telemedicine, and information exchanges can provide game-changing benefits for providers and patients alike. However, with increased sharing comes increased risks to both the security and the privacy of patient information. Most digital health and telemedicine companies are aware of data security and breaches. However, an arguably more important compliance area is the intentional sharing of protected health information (PHI) with third parties, whether for data mining, research, or marketing and purposes. Because data sharing and data mining will only continue to grow across the health care industry, providers and vendors must understand when and how they can share PHI, including monetization opportunities, and when they must obtain the patient's express authorization.
Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments. It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers). As health data contain sensitive private information, including the identity of patient and carer and medical conditions of the patient, proper care is required at all times. Leakage of these private information affects the personal life, including bullying, high insurance premium, and loss of job due to the medical history. Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data. Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision health is essential. In this regard, firstly, this paper explores the regulations, ethical guidelines around the world, and domain-specific needs. Then it presents the requirements and investigates the associated challenges. Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects. Finally, it illustrates the best available techniques for precision health data security and privacy with a conceptual system model that enables compliance, ethics clearance, consent management, medical innovations, and developments in the health domain.
A viewpoint review published in JAMA examined the adequacy of the Health Insurance Portability and Accountability Act (HIPAA) in the "big data" era of MyHealthEData and similar electronic record systems. Introduced by the Trump administration in March 2018, the MyHealthEData initiative seeks to broaden patient access to electronic health records and insurance claims information. MyHealthEData and similar electronic systems allow patients to share health information at their discretion, an approach which may enable individuals to identify optimal treatment plans and network with health services. However, the digital sharing of health-related information raises new privacy concerns, not the least of which is the prospect of "invasive marketing" and "discriminatory practices that evade…law." In the present day, the authors assert, HIPAA-protected data owns a "diminishing share" of health information stored electronically, and privacy regulations should be amended accordingly.
David Feinberg, Google's Vice President of Healthcare, recently described "a search bar on top of ... [ ] your [electronic health records] that needs no training," on stage at a conference in Las Vegas. Google is testing a service that would use its search and artificial intelligence technology to analyze patient records for Ascension, the largest nonprofit health system in the U.S., according to documents about the efforts reviewed by Forbes. Called "'Nightingale," the Google-Ascension project indicates that Google's push into health analysis is farther along than previously believed, even as the company has faced a growing backlash over health-related privacy concerns. Ascension said in a statement that all its work with Google complies with privacy law and is "underpinned by a robust data security and protection effort, which Google echoed in its own blog post later Monday, including that "patient data cannot and will not be combined with any Google consumer data. " The Wall Street Journal first published details of the Ascension partnership earlier on Monday.
Artificial intelligence has the potential to transform health care. It can enable health care professionals to analyze health data quickly and precisely, and lead to better detection, treatment, and prevention of a multitude of physical and mental health issues. Artificial intelligence integrated with virtual care -- telemedicine and digital health -- interventions are playing a vital role in responding to Covid-19. Penn Medicine, for example, has designed a Covid-19 chatbot to stratify patients and facilitate triage. Penn is also using machine learning to identify patients at risk for sepsis.