Dr. Google


We usually meet traditionalist doctors who would laugh when patients appear to know what ails them after consulting the internet. But modern medicine is no longer laughing: “big data” which is the back bone of Google is getting to be the gold standard in the practice of modern medicine.

How Big Data Is Transforming Medicine by  Bernard Marr  

When we visit our doctor or go into hospital, we have faith in the knowledge that the healthcare professionals involved are treating us according to proven scientific methods, otherwise known as evidence-based medicine (EBM). This means they’re prescribing drugs or selecting treatment methods that have proven successful in clinical research.

Although the term ‘evidence-based medicine’ only dates back to the early 1990s, the concept itself is much older. Controlled trials were routinely being conducted as early as the 1940s, and clinical knowledge and expertise was already being disseminated in medical journals and textbooks long before that. (In fact, the oldest medical journal still running today, The New England Journal of Medicine, was founded in 1812. Even older, the first official clinical trial was conducted in 1747, into the treatment of scurvy in sailors.)

Clinical trials and studies are all about conducting research into disease and conditions, and the various treatment methods that may ease symptoms or eradicate the illness altogether—they explore which treatments work best for which illnesses and in which groups of patient. All around the world, EBM is the established standard for the provision of healthcare. But, in the age of big data, that might be about to change.

Clinical trials work by testing new treatments in small groups at first, looking at how well the treatment works and to identify any side effects. If a trial proves promising, it is expanded to include larger groups of people. Often the trial will include comparing the new treatment to other treatments by separating patients into different groups, each trialing a different treatment. This is usually done by a process called randomization, where patients are assigned to the various groups randomly.

In order to safeguard participants and improve reliability, clinical trials have to meet rigorous scientific standards. However, that’s not to say there is no risk of methodological flaws, or that the small-ish populations used in clinical trials always generalize well outside of a particular study. This is where big data can help. By mining the world of practice-based clinical data—i.e. actual patient records—for information on who has what condition and what treatments are working, we could learn a lot about the way we care for individuals.

One company, California-based cognitive computing firm Apixio, has firmly set its sights on enabling healthcare providers to learn from practice-based evidence to individually tailor care. As Apixio CEO Darren Schulte explains, “We can learn more from the practice of medicine and refine our approach to clinical care. This gets us closer to a ‘learning healthcare system’. Our thinking on what actually works and what doesn’t is updated with evidence from the real-world data.”

A staggering 80% of medical and clinical information about patients is formed of unstructured data, such as written physician notes, consultant notes, radiology notes, pathology results, discharge notes from a hospital, etc.


Schulte, a physician who was Apixio’s Chief Medical Officer before being appointed CEO, says, “If we want to learn how to better care for individuals and understand more about the health of the population as a whole, we need to be able to mine unstructured data for insights.”

Electronic Health Records (EHRs) have been around for a while but, with the data stored across a number of different systems and formats, they are not really designed with analysis in mind. So before Apixio can even analyze any data, they first have to extract the data from these various sources (such as GP surgeries, hospitals, government Medicare records, etc.). Then they need to turn that information into something that computers can analyze. Clinician notes can come in many different formats—some are handwritten and some are in a scanned PDF file format—so Apixio uses OCR (optical character recognition) technology to create a textual representation of that information that computers can read and understand.

The data can then be analyzed at an individual level to create a patient data model, and it can also be aggregated across the population in order to derive larger insights around the disease prevalence, treatment patterns, etc.

Schulte explains, “We create a ‘patient object’, essentially a profile assembled using data derived by text processing and mining text and coded healthcare data. By creating this individual profile and grouping together individuals with similar profiles, we can answer questions about what works and what doesn’t in those individuals, which becomes the basis for personalized medicine.”

Getting healthcare providers and health insurance plans to share data can be a challenge, which Apixio overcomes by making sure they offer real value in return for access to data. As Schulte says, “Unless you solve a real critical problem today, none of these organizations will give you access to any real amount of data.” Thus, the focus needs to be on tangible results and solving problems as opposed to adding to all the hype around big data. Schulte confirms this: “CIOs at hospitals don’t often see a lot of problems actually being solved using big data. They see a lot of slick dashboards which are not very helpful to them. What’s helpful is actively solving problems today.”

Another big challenge when it comes to patient health data is security, especially after some high-profile health data breaches. In 2014, medical records accounted for 43% of all data stolen and the healthcare sector has seen the biggest increase in data theft since 2010 (far more so than business or government sectors). Schulte refers to data security as “table stakes”, meaning it is an essential requirement for anyone wanting to operate in the healthcare big data arena and confirms, “For every new contract we have to demonstrate our security.” Patient data must be encrypted at rest and in-transit, and Apixio never exposes personal health information (PHI) unless access is absolutely needed by Apixio staff.

Can practice-based medicine oust evidence-based medicine as the gold standard in healthcare provision? Perhaps not. But there’s no doubt that we’re on the verge of exciting new ways to understand, treat and prevent diseases. As Schulte puts it, “We’re in a new world in terms of the way healthcare is going to be practiced, based upon these data-driven insights.” A future which combines both evidence-based and practice-based medicine is likely to produce the best outcomes for patients—and, at the end of the day, that’s all any healthcare professional wants.

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