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.
The UAE government is leading the way in establishing the necessary integrated and secure data ecosystem to expedite the implementation of future technologies like Artificial Intelligence (AI) in healthcare, which use data from many disparate sources to produce unprecedented services that will transform all aspects of people's wellness and everyday life. AI and machine learning offers hope in reducing the risk and impact of cyber-attacks on patient data, but also opens doors to potential wrong doers – "The Bad Guys" – by its very nature. Security threats are, and always have been, major concerns to healthcare organizations due to the value and vulnerability of the clinical data that is being recorded and distributed. The value of the data comes from the fact that it directly affects our ability to safely treat patients. Due to its content and historical nature it can be very big, so it takes a long time to rebuild, and it contains more than just clinical data.
REUTERS: Alphabet Inc's Google signed its biggest cloud computing customer in healthcare yet, according to an announcement on Monday (Nov 11), gaining with the deal datasets that could help it tune potentially lucrative artificial intelligence tools. The Wall Street Journal earlier reported Google teaming up with Ascension to collect personal health-related information of millions of Americans across 21 states. The partnership will also explore artificial intelligence and machine learning applications to help improve clinical effectiveness as well as patient safety, Ascension said in a statement. Google Cloud Chief Executive Officer Thomas Kurian has made it a priority in his first year on the job to aggressively chase business from leaders in six industries, including healthcare. The company previously had touted smaller healthcare clients, such as the Colorado Center for Personalized Medicine.
The Healthy Nevada Project, developed by Renown Institute for Health Innovation (Renown IHI), is one of the first community-based population health studies in the United States. By combining genetic data, environmental data and individual health information, researchers and physicians are gaining new insight into population health, enabling personalized health care while improving the health and well-being of entire communities in Nevada. The Project comes at a time when the state continues to struggle with poor health outcomes and excess costs. Nevada ranks near the bottom of overall health rankings in the U.S. and suffers from high mortality rates for chronic conditions like heart disease, cancer and chronic respiratory disease. "This was our call to action," says Dr. Anthony Slonim, President and CEO of Renown Health.
Precision Medicine (PM) is an emerging approach that appears with the impression of changing the existing paradigm of medical practice. Recent advances in technological innovations and genetics, and the growing availability of health data have set a new pace of the research and imposes a set of new requirements on different stakeholders. To date, some studies are available that discuss about different aspects of PM. Nevertheless, a holistic representation of those aspects deemed to confer the technological perspective, in relation to applications and challenges, is mostly ignored. In this context, this paper surveys advances in PM from informatics viewpoint and reviews the enabling tools and techniques in a categorized manner. In addition, the study discusses how other technological paradigms including big data, artificial intelligence, and internet of things can be exploited to advance the potentials of PM. Furthermore, the paper provides some guidelines for future research for seamless implementation and wide-scale deployment of PM based on identified open issues and associated challenges. To this end, the paper proposes an integrated holistic framework for PM motivating informatics researchers to design their relevant research works in an appropriate context.
A consequence of the fragmented and siloed healthcare landscape is that patient care (and data) is split along multitude of different facilities and computer systems and enabling interoperability between these systems is hard. The lack interoperability not only hinders continuity of care and burdens providers, but also hinders effective application of Machine Learning (ML) algorithms. Thus, most current ML algorithms, designed to understand patient care and facilitate clinical decision-support, are trained on limited datasets. This approach is analogous to the Newtonian paradigm of Reductionism in which a system is broken down into elementary components and a description of the whole is formed by understanding those components individually. A key limitation of the reductionist approach is that it ignores the component-component interactions and dynamics within the system which are often of prime significance in understanding the overall behaviour of complex adaptive systems (CAS). Healthcare is a CAS. Though the application of ML on health data have shown incremental improvements for clinical decision support, ML has a much a broader potential to restructure care delivery as a whole and maximize care value. However, this ML potential remains largely untapped: primarily due to functional limitations of Electronic Health Records (EHR) and the inability to see the healthcare system as a whole. This viewpoint (i) articulates the healthcare as a complex system which has a biological and an organizational perspective, (ii) motivates with examples, the need of a system's approach when addressing healthcare challenges via ML and, (iii) emphasizes to unleash EHR functionality - while duly respecting all ethical and legal concerns - to reap full benefits of ML.
The pace of change in the field of artificial intelligence (AI) is accelerating so quickly that regulators and legislators are having a hard time keeping up. And that's especially true in the healthcare industry, where rapid new advances in AI technology are already starting to make healthcare professionals re-think the efficacy of the landmark 1996 Healthcare Insurance Portability and Accountability Act (HIPAA) and consider possible new protections for health data privacy. One major result of HIPAA was the creation of the Privacy Rule, which establishes national standards to protect individuals' medical records and other personal health information. According to HIPAA, covered entities include health plans, health care clearinghouses, and health care providers who electronically transmit any health information. Under the terms of this federal law, individually identifiable health information was known as protected health information (PHI).
Improvements in artificial intelligence hold the potential to put personal health data at risk, a new study shows. Advances in artificial intelligence have created new threats to the privacy of health data, a new UC Berkeley study shows. The study, led by professor Anil Aswani of the Industrial Engineering & Operations Research Department (IEOR) in the College of Engineering and his team, suggests current laws and regulations are nowhere near sufficient to keep an individual's health status private in the face of AI development. The research was released today on JAMA Network Open. In the work, which was funded in part by UC Berkeley's Center for Long-Term Cybersecurity, Aswani shows that by using artificial intelligence, it is possible to identify individuals by learning daily patterns in step data (like that collected by activity trackers, smartwatches and smartphones) and correlating it to demographic data.
Advances in artificial intelligence have created new threats to the privacy of people's health data, a new University of California, Berkeley, study shows. Led by UC Berkeley engineer Anil Aswani, the study suggests current laws and regulations are nowhere near sufficient to keep an individual's health status private in the face of AI development. The research was published Dec. 21 in the JAMA Network Open journal. The findings show that by using artificial intelligence, it is possible to identify individuals by learning daily patterns in step data, such as that collected by activity trackers, smartwatches and smartphones, and correlating it to demographic data. The mining of two years' worth of data covering more than 15,000 Americans led to the conclusion that the privacy standards associated with 1996's HIPAA (Health Insurance Portability and Accountability Act) legislation need to be revisited and reworked.