Holy Grail of medical data and privacy
it has been said that in today’s economy, data are the new oil. While that statement is debatable, no one denies the fact that big data, and the ability to analyze more information than ever before, is becoming increasingly crucial in today’s dynamic business environment.
however, it is not just the business sector that is positioned to benefit from big data, healthcare has much to gain as well. the issue though, is that in healthcare, data touch on a whole new level of sensitivity. While everyone appreciates the ability to analyze data and extract information that could save lives, that ability is balanced out by the conflicting need for absolute patient privacy.
so what we have here is the need to use big data in healthcare on the one hand, and the need to never expose patient information on the other. seems like a gridlock of a situation that has no solution. enter mdClone. mdClone lets doctors and researchers evaluate medical data quickly, using statistically accurate data that cannot be reverse-engineered to identify specific patients.
mdClone was founded in 2016 by a team that includes Ceo Ziv ofek. ofek previously founded and served as chief innovation officer of dbmotion, a successful israeli company that managed patient records and was acquired by allscripts, the publicly-traded us company, for $235 million in 2013.
in the short time since mdClone was founded in 2016, it has generated millions of dollars in revenue and captured more than 80% of the israeli market across major hospitals and health systems including assuta medical Center, Chaim sheba medical Center, Clalit healthcare services, maccabi healthcare services, rambam healthcare Campus, and telaviv sourasky medical Center (ichilov).
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 have 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. usually this approach leverages data science tools including artificial intelligence or machine learning.
a doctor or researcher can cull through hundreds of thousands of data points and arrange them by factors they share. When large amounts of data are 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. more recently, the european union has issued directives under the general data protection regulation (gdpr) that effectively do the same thing.
mdClone has developed a platform it calls the healthcare data sandbox to enable widespread use of big data while protecting patient privacy. 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 are 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. mdClone’s solution sits at the crucial point between data-gatherers and data-users. By breaking the balancing act between patient privacy and data utility, mdClone is in a prime position to dramatically increase the use of big data, turning it into information which can save lives.