Holy Grail of medical data and privacy

Holy Grail of medical data and privacy

Publication date: Aug 29, 2019

Since 2018 it has marketed its platform in the US, and customers already include Intermountain Healthcare in Utah, the Regenstrief Institute in Indiana, and the Washington University School of Medicine in St. Louis, Missouri. MDClone recently closed a $26 million Series B funding round led by aMoon, with additional funding from OrbiMed Israel Partners and Lightspeed Venture Partners, both of which were already shareholders in the company. The firm has 50 employees, most of whom work in its Beersheba headquarters, and itplans to double its headcount in the next 18 months. In recent years, big data has taken a leading role in patient care and medical research. Doctors who are trying to diagnose an illness or identify the right treatment for a patient, and research scientists who are trying to understand how a disease spreads, increasingly see big data as a valuable tool to guide their efforts. When large amounts of data is organized in this way, artificial intelligence, for instance, can be used to identify relevant factors which a human may not have been able to isolate. UTILIZING DATA this way is crucial for advancing patient care, enabling caregivers to provide treatments according to the statistical likelihood that a patient will respond (precision medicine), and dramatically improving our understanding and treatment of diseases, something we’ve already seen for chronic conditions including cancer, kidney disease, diabetes, and depression. However, patient privacy laws in the US and Europe have become a major obstacle to large-scale use of patient data. In the US, the Health Insurance Portability and Accountability Act of 1996 (HIPAA) severely limits the use of a patient’s information. As part of the platform, its synthetic data engine allows doctors and researchers to access medical information without compromising patient privacy and confidentiality. Users of the platform can formulate their own queries – inquiring about different patient populations of interest – and the platform is able to search through massive amounts of data to not only find the population, but to present the data in such a way which preserves all the relevant properties of the patients without any ability to identify specific individuals. Think of it as a Google for medical information, only with total privacy and anonymity. Healthcare professionals are using the platform to freely access and analyze the massive amount of data generated by health systems, health insurance companies, pharmaceutical and medical device companies, and other creators or users of electronic health records. Not only are internal users able to access data – that is, doctors and researchers accessing data at their own institutions – but MDClone allows data to be shared among collaborators who work externally, at other hospitals, perhaps, or even start-ups. In effect, MDClone’s patented synthetic data engine creates a fictitious set of -people” based merely on the statistical properties extracted from an actual set of people and without any one-to-one connection between the real people and fake ones. Not only is the data safe and reliable, it can be accessed in minutes and not weeks and months, both because of the strength of the platform and the use of non-human subject synthetic data (the fake -people”) which do not require time-consuming authorizations from an organization, such as the Helsinki Committee or Institutional Review Board, regulators tasked with protection of patients. Use of big data has already shown significant value to improving patient care, and the amount of data being used, relative to the data available, is just a tiny fraction.

Concepts Keywords
Artificial Intelligence Artificial intelligence
Beersheba Medicine
Big Data Medical privacy
Cancer Big data
CEO DbMotion
Clalit Medical record
Confidentiality Articles
Depression Health informatics
Diabetes Chronic conditions
Europe Illness
European Union Treatment diseases
GDPR Data healthcare gain
Google Healthcare touches sensitivity
Gridlock Healthcare hand
Healthcare
Helsinki
HIPAA
Holy Grail
Indiana
Insurance
Intermountain Healthcare
Israel
Kidney Disease
Lightspeed Venture Partners
Missouri
Oil
Pharmaceutical
Precision Medicine
Prime
Privacy
Rambam
Reverse Engineered
Tel Aviv
The Firm
Utah
Washington

Semantics

Type Source Name
gene UNIPROT SERPINA3
drug DRUGBANK Nonoxynol-9
disease MESH privacy
gene UNIPROT RNF128
disease MESH depression
disease DOID kidney disease
disease MESH kidney disease
disease DOID cancer
gene UNIPROT LARGE1
gene UNIPROT ETV6
gene UNIPROT FHL5
gene UNIPROT NR4A2
gene UNIPROT ALG3
gene UNIPROT HMBS
gene UNIPROT SET

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