Big Data in Healthcare - Hype or Reality

The Big Data Questions

Big data is generating a lot of hype in every industry including healthcare. People are looking for answers to questions like:

    When will I need big data?
    What should I do to prepare for big data?
    What’s the best way to use big data?
    What is Health Catalyst doing with big data?

It’s important to separate the reality from the hype and clearly describe the place of big data in healthcare today, along with the role it will play in the future.

Big Data in Healthcare Today



A number of use cases in healthcare are well suited for a big data solution.
Some academic- or research-focused healthcare institutions are either experimenting with big data or using it in advanced research projects.
This presentation will examine what’s being done to simplify big data and make it more accessible.

A Brief History of Big Data in Healthcare

In 2001, Doug Laney, now at Gartner, coined the term “the 3 V’s” to define big data:

• Volume
• Velocity
• Variety

Other analysts argued that this is too simplistic but for this purpose let’s start here.









EMRs alone collect huge amounts of data, but according to Brent James of Intermountain Healthcare most of the data is for recreational purposes.
Our work with health systems shows that only a small fraction of the tables in an EMR database (perhaps 400 to 600 tables out of 1000s) are relevant to the current practice of medicine and its corresponding analytics use cases.

There is certainly variety in the data, but most systems collect very similar data objects with an occasional tweak to the model.
That said, new use cases that support genomics will certainly require a big data approach.

Health Systems Without Big Data

Most health systems can do plenty today without big data, including meeting most of their analytics and reporting needs.
We haven’t come close to stretching the limits of what healthcare analytics can accomplish with traditional relational databases—and using these databases effectively is a more valuable focus than worrying about big data.



Most healthcare institutions are swamped with some very pedestrian problems such as regulatory reporting and operational dashboards.
As basic needs are met and some of the initial advanced applications are in place, new use cases will arrive (e.g. wearable medical devices and sensors) driving the need for big-data-style solutions.

Barriers Exist for Using Big Data

Expertise and Security

Several challenges with big data have yet to be addressed in the current big data distributions.
Two roadblocks to the general use of big data in healthcare are the technical expertise required to use it and a lack of robust, integrated security surrounding it.


Expertise  

The value for big data in healthcare today is largely limited to research because using big data requires a very specialized skill set.
Hospital IT experts familiar with SQL programming languages and traditional relational databases aren’t prepared for the steep learning curve and other complexities surrounding big data.

Data scientists are usually Ph.D.-level thinkers with significant expertise.
These experts are hard to come by and expensive, and only research institutions usually have access to them.
Data scientists are in huge demand across industries like banking and internet powers with deep pockets.

The good news is, thanks to changes with the tooling, people with less-specialized skillsets will be able to easily work with big data in the future.
Big data is coming to embrace SQL as the lingua franca for querying. And when this happens, it will become useful in a health system setting.