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Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy

arXiv.org Artificial Intelligence

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.


Inside Google's Quest for Millions of Medical Records

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Cerner was interviewing Silicon Valley giants to pick a storage provider for 250 million health records, one of the largest collections of U.S. patient data. Google dispatched former chief executive Eric Schmidt to personally pitch Cerner over several phone calls and offered around $250 million in discounts and incentives, people familiar with the matter say. Google had a bigger goal in pushing for the deal than dollars and cents: a way to expand its effort to collect, analyze and aggregate health data on millions of Americans. Google representatives were vague in answering questions about how Cerner's data would be used, making the health-care company's executives wary, the people say. Eventually, Cerner struck a storage deal with Amazon.com The failed Cerner deal reveals an emerging challenge to Google's move into health care: gaining the trust of health care partners and the public.


The Double-edged Sword of AI and Machine Learning on Healthcare Data Security

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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.


Precision Medicine Informatics: Principles, Prospects, and Challenges

arXiv.org Artificial Intelligence

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.


Machine learning in healthcare -- a system's perspective

arXiv.org Artificial Intelligence

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.


Advances in artificial intelligence threaten privacy of people's health data

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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.


Artificial intelligence advances threaten privacy of health data

#artificialintelligence

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.


Advances in AI threaten health data privacy: Study

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Advances in artificial intelligence (AI) have created new threats to the privacy of health data, a study has found. The study, published in the journal JAMA Network Open, 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 led by professor Anil Aswani from the University of California -- Berkeley in the US, shows that by using AI, 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. The mining of two years' worth of data covering over 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. "We wanted to use NHANES (the National Health and Nutrition Examination Survey) to look at privacy questions because this data is representative of the diverse population in the US," Aswani said.


Ramos and Aiken: How artificial intelligence can improve health care

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Policy debates over how to solve problems around access to family doctors, wait times for elective surgery, home care, transfer to long-term care, tracking the over-prescription of opioids and many other serious health dilemmas facing Canadians rarely consider the role artificial intelligence (AI) can and will play in offering solutions. But the potential to realize the benefits of AI requires a proactive policy strategy that is geared to the future rather than a reactive approach, constantly focused on managing current crises. This means solutions for tomorrow rather than today and also will require parsing out how to recognize, trade and access the commodity that drives the "gig economy" – data. The Fraser Institute warned that in the next decade Canada's doctor shortage will only worsen, largely because of an increase in the number retiring physicians that will not be replaced fast enough by new or foreign trained doctors. The Canadian Institute for Health Information found that although wait times are improving for hip surgery, they are getting worse for cataract surgery and are remaining constant for a number of other procedures.


How AI delivers powerful insights through Healthcare data

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Artificial intelligence (AI) propelled by increasing availability of data and analytics is creating a revolution in the way technology works in solving complex problems. The fact that it utilizes, both structured and unstructured data to deliver powerful, conclusive result makes it highly sought after in areas of healthcare, entertainment, finance, transportation and more. Thanks to AI, the voluminous data which was previously untapped has now been unplugged. Coupled with predictive analysis, through AI massive amounts of data have been scrubbed to produce results that have made a paradigm shift in the way healthcare operates for all – providers, patients and professionals. What is AI actually and how does it work in healthcare?