By Clarity Insights,
Clarity Insights is a strategic partner to the nation's leading data-driven brands.

 

Big Data in Healthcare Is Nothing New.

In fact, it has existed for a very long time. It’s just been locked up in written medical charts, carefully sorted in giant folders and binders in decade-old cabinets. The government-induced frenzy of EMR adoption may have not made the data any more ubiquitous or accessible, but it is a good first step leading us to what the future of healthcare data holds.

The last decade has seen a major acceleration of the amount of data we have and the time needed to analyze it. Moore’s law does not only mean double computer speed with half the cost every year and a half, but it also means that our number-crunching and insight-generating abilities are going to exponentially get stronger over the next few years.

In 2015, I have travelled across the States and took a voyage into the future by attending many of the major digital health conferences, interviewing and writing about amazing entrepreneurs. I ended up working at Clarity Solution Group which gives me the opportunity to work with data pioneers in consulting some of the nation’s largest healthcare payers and providers while understanding what is being done with healthcare data right now.

Healthcare is currently playing catch up with the power of data technology, but as 2015 very rapidly comes to a close, I’d like to share with you the 3 biggest ideas I have encountered about data in our brave new healthcare.

1. Health and Life Are Mosaic

We recently realized that we’ve been doing medicine all wrong. As it turns out, health is not determined by what goes on in the medical facility but by our life decisions and habits that are predominant in all life’s moments except when we are at the doctor’s office.

We are figuring out that what works for the Fortune 500 executive that lives in New York City, certainly does not apply to the newly-divorced mother-of-two living in Southern Nevada.

Poverty, emotional well being, perception of health, sexual activity, feeding habits, and stressful jobs all play THE major role in making us healthier or sicker. A recent study in the U.K. measured the causes of premature death and found that 20% was because of the healthcare system’s fault, 20% were genetic and the remaining 60% were due to these life stressors which are referred to as “The social determinants of health” or the “Unmentionables”.1

The good news is that we have much more data about these elements than ever before. The open data movement adopted by the CDC and healthcare.gov makes a ton of environmental, demographic, biometric, and public health data readily available online for free.

The big questions now are: How can we couple this public health data with its counter part in those Electronic Medical Records? How can we present bits of these public datasets to the physician allowing for timely intervention and precision medicine? Conversely, how can we leverage what goes on in the medical realm to give public health entities more focus on their outreach activities?

This gathering of different datasets together is referred to as the Mosaic Effect, which is making data analysis sexier than ever before. One big challenge with blending different datasets together however, is that new security breaches become possible. This brings me to the next big idea.

 

2. Blockchain Is the Ideal Electronic Medical Record

By now you have probably heard of Bitcoin, a new age ‘crypto currency’ which for better or for worse has challenged the world to re-think economics. The innovation behind bitcoin is not really the currency per se, but the technology it was built on: The Blockchain.

Think of Blockchain as a large database or Excel sheet, which stores transactions one after the other in a ledger format, and these transactions are validated by the users of the system as opposed to a centralized bank. Those who are on the system can store their information, keep it encrypted and give access privileges to certain users, while keeping their data anonymous for the most part.

Now tell me that this does not sound like the perfect EMR! Imagine if you can own and protect your own medical data, give access to your doctors to see it, and then anonymously choose to donate some of that data to researchers.

The fact that you actually own your data is not to be taken for granted. Most proprietary EMR systems now still keep the data locked up digitally, and what is worse is that EMRs are not interoperable and most of them do not "talk" to each other.

Blockchain allows for storage of data which is totally open-source, too. You can access, modify and analyze it regardless of which hospital you go to or where you live.

So why isn’t this mainstream already? Well, the government spent billions of dollars on the wrong kind of EMR, and the market is full of proprietary systems that don’t talk to each other and are each competing for hospitals -- essentially fragmenting healthcare data even further.

A consumer-led approach to Blockchain built around a viable business model, may disrupt the current EMR modalities and allow for a universal, secure and interoperable data architecture within which we can store very large and different healthcare datasets.

3. The Google Now of Healthcare

If we accomplish the previous 2 points, then we would have aggregated different mosaic datasets to accurately represent our health, fused them with clinical data, and stored it all on a scalable, secure and de-centralized system.

Now what do we do with it?

Let me quickly take you back to 1956, when IBM’s Arthur Samuel was the first person to teach his computer how to fish rather than give it a fish. This concept developed rapidly and is referred to as Machine Based Learning: Instead of writing code that tells the computer what to do, you teach it the basic principles.

In Arthur’s case, he taught it the general rules of checkers. The computer eventually beat him and, in 1962, it beat the Connecticut state champion. Computers then were able to beat world chess champions, challenge the Turing test, and IBM’s Watson famously beat humans on Jeopardy.

We see that idea being applied everywhere. Amazon suggests other books, Facebook and LinkedIn suggest people to know, and Google Now tells you when you need to leave home to catch a flight and offers articles and news based on your interests.

This idea has a wide range of applications in healthcare. What if we can take genomic data, blend it with radiological scans and start predicting the diagnosis of cancer before it spreads? Or what if we can use information about when buildings where constructed, alongside blood chemistry data to predict how many people with lead poisoning might show up in a clinic?

These two scenarios are actually real. The former is based on work being done by a deep learning company, Enlitic, and the latter being an example from the Illinois Hospital Association.

By letting a computer learn from the raw data as opposed to how we humans have learned to interpret medicine and healthcare, we can generate much more profound insights and explore in ways we cannot even conceive now.

The future of big data in healthcare is spectacular, but our healthcare data stream currently isn’t. The exponential rise of technology allows for a large number of resources to be leveraged while rethinking healthcare. This bold future is only limited by our open mindedness and our regulation.

Where do you and your organization stand on that future trajectory? Are you ready to embrace the change?

Omar Shaker is a physician, writer, and data analyst. After realizing the potential of exponential technologies to reshape the inefficiencies of healthcare, he left medicine and moved to San Francisco to immerse himself within the network of entrepreneurs in Silicon Valley, while working on technology projects of his own. Mr. Shaker is currently a consultant with the Healthcare Practice of Clarity Solution Group.

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1 J. Michael McGinnis, Pamela Williams-Russo and James R. Knickman The Case For More Active Policy Attention To Health Promotion Health Affairs, 21, no.2 (2002):78-93 doi: 10.1377/hlthaff.21.2.78

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