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.
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 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.
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 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 (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.
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.
Imagine you're not feeling well and you trudge into a doctor's office. As usual, your blood pressure, temperature, weight, and blood tests are taken, and the results are entered into your record. After asking some computer-assisted questions about your symptoms, the doctor has the system churn through all this information to make recommendations for treatments, medicines, and lifestyle changes to make you better. The data-informed evaluation is tailored specifically to you and costs less than a visit to the doctor's office today. This level of personalized medicine is the promise behind the growing influence of machine learning and artificial intelligence (AI) in healthcare.
To improve the security of patient health data, it seems only natural that a healthcare organization would choose security software modeled after the human immune system. There's no magic bullet or single software that can fully secure your healthcare organization – but there are strategies that can help. In this e-guide, get free advice from healthcare CIOs and CISOs to help bolster your cybersecurity plan. You forgot to provide an Email Address. This email address doesn't appear to be valid.
On May 12, the largest ransomware outbreak in history took place, targeting 300,000 machines in 150 countries, with the U.K.'s National Health Service (NHS) taking the brunt of the attack. In fact, 48 hospital trusts in the U.K. were targeted by the NSA cyber weapon-powered WannaCry ransomware, in addition to an unknown number of hospitals in the United States. Further, the Health Information Trust Alliance (HITRUST) reported that not just hospital machines were infected, but also medical devices from both Bayer and Siemens. By shutting down systems, communication channels and equipment, cybercriminals locked healthcare professionals out of their EHRs, forced them to cancel appointments and even turned away emergency patients. Unfortunately, this is just another example of the healthcare industry being targeted by increasingly sophisticated and frequent ransomware attacks.