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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.


China rolls out fresh data collection campaign to combat coronavirus - Reuters

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SHANGHAI (Reuters) - China's local governments are ramping up surveillance efforts with new data collection campaigns to better trace residents' moves in public areas, seeking to curb the coronavirus outbreak but heightening privacy concerns. At least 15 provinces and cities with a combined population of over 358 million have announced such "big data" measures this month, adding to a host of monitoring tools already being used, such as facial recognition and phone data tracking. Visitors to office buildings, shopping malls, residential compounds and metro systems are now being asked to scan QR codes using their mobile phones and fill in forms asking for information such as their travel history and body temperature, according to residents and local media reports. Users fill in a questionnaire to obtain a obtain colour-based QR code which then acts as guidance at checkpoints as to whether the person should be quarantined or let through. The outbreak, traced to the Hubei provincial capital of Wuhan, has killed 2,715 so far and stricken about 78,000 in mainland China.


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.


Google Secretly Tests Medical Records Search Tool On Nation's Largest Nonprofit Health System, Documents Show

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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.


Google's healthcare partnership sparks fears for privacy of millions

The Guardian

Google's announcement of a partnership with a major healthcare provider raises fresh privacy concerns as the tech company expands its footprint into the healthcare industry. Monday's announcement comes after the Wall Street Journal revealed Google had won access to health-related information of millions of Americans across 21 states through the partnership with Ascension – the second-largest healthcare system in the US. The Journal reported that the data involved in the project includes lab results, doctor diagnoses and hospitalization records, among other categories, and amounts to a complete health history, including patient names and dates of birth. The collaboration, code-named "Project Nightingale", began in secret last year, according to the Journal. Google's parent company, Alphabet, on Monday officially signed Ascension, its biggest cloud computing customer in healthcare yet.


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.


Alexa, where are the legal limits on what Amazon can do with my health data? – TechCrunch

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The contract between the UK's National Health Service (NHS) and ecommerce giant Amazon -- for a health information licensing partnership involving its Alexa voice AI -- has been released following a Freedom of Information request. The government announced the partnership this summer. But the date on the contract, which was published on the gov.uk contracts finder site months after the FOI was filed, shows the open-ended arrangement to funnel nipped-and-tucked health advice from the NHS' website to Alexa users in audio form was inked back in December 2018. The contract is between the UK government and Amazon US (Amazon Digital Services, Delaware) -- rather than Amazon UK. Nor is it a standard NHS Choices content syndication contract.


Automatic end-to-end De-identification: Is high accuracy the only metric?

arXiv.org Machine Learning

De-identification of electronic health records (EHR) is a vital step towards advancing health informatics research and maximising the use of available data. It is a two-step process where step one is the identification of protected health information (PHI), and step two is replacing such PHI with surrogates. Despite the recent advances in automatic de-identification of EHR, significant obstacles remain if the abundant health data available are to be used to the full potential. Accuracy in de-identification could be considered a necessary, but not sufficient condition for the use of EHR without individual patient consent. We present here a comprehensive review of the progress to date, both the impressive successes in achieving high accuracy and the significant risks and challenges that remain. To best of our knowledge, this is the first paper to present a complete picture of end-to-end automatic de-identification. We review 18 recently published automatic de-identification systems -designed to de-identify EHR in the form of free text- to show the advancements made in improving the overall accuracy of the system, and in identifying individual PHI. We argue that despite the improvements in accuracy there remain challenges in surrogate generation and replacements of identified PHIs, and the risks posed to patient protection and privacy.


Identifying and working with sensitive healthcare data with Amazon Comprehend Medical Amazon Web Services

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SUBJECTIVE: Carlos came to the ED via ambulance accompanied by son, Jorge. He is a 50 yo male who was working at Food Corp when he had sudden onset of palpitations. Carlos stated his fater, Diego, also had palpitations through his life.


Advancement of AI Opens Health Data Privacy to Attack

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Advances in artificial intelligence have created new threats to the privacy of health data, according to a new study by University of California, Berkeley researchers. University of California, Berkeley (UC Berkeley) researchers have found that artificial intelligence (AI) innovations have created new threats to health data privacy against which current laws and regulations cannot adequately safeguard. The researchers demonstrated that AI can be used 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. Said UC Berkeley's Anil Aswani, "In principle, you could imagine Facebook gathering step data from the app on your smartphone, then buying healthcare data from another company and matching the two. Now they would have healthcare data that's matched to names, and they could either start selling advertising based on that or they could sell the data to others."