Creating healthcare data is easy. Wrangling it so that it is sharable, useable, and valuable is challenging. In fact, it may be the one thing that prevents healthcare organizations from utilizing advanced technologies like AI, machine learning, and predictive analytics. The good news is that it can be done, you just have to choose the right data animal to tame.
Explosive Data Growth
RBC Capital Markets projects that “by 2025, the compound annual growth rate of data for healthcare will reach 36%” much higher than for most industries including manufacturing, financial services and media + entertainment. In 2018 Statistica estimated healthcare was producing 2,314 exabytes of data (1 exabyte is 1 billion GB).
Do the math and you end up with an incomprehensible amount of data.
“It would be understatement to say that lots of data is generated by healthcare,” said Kevin Campbell, Founding Partner and CEO at DTA Healthcare Solutions who sat down recently with Healthcare IT Today. “Unlike retail, manufacturing, and many other industries, each ‘transaction’ in healthcare – aka an encounter – generates many multiples of data that a financial transaction does.”
Plus, Campbell rightly pointed out that in healthcare we deal with more than just data: the information represents lives, diseases, and other intimate details. In other words, data = people in healthcare and as such, deserves special attention.
Taming Healthcare Data
Being overwhelmed by the data deluge in healthcare is understandable. Not only are we producing data at a phenomenal rate, but healthcare organizations are also resource-constrained when it comes to data management and data governance.
Healthcare organizations therefore need to be efficient if they are to tame the wild animal that is healthcare data. The key, according to Campbell, is knowing which animal to go after.
“We have to recognize what can be tamed and what can’t,” explained Campbell. “People can get overwhelmed when they look at the overall volume of data that they want. If you try to tame everything you see, you’ll be eaten alive.”
Based on his team’s work with many healthcare organizations, Campbell recommends categorizing your data into one of these four groupings:
- Unknown/Future Data. Data that science hasn’t caught up to yet or data that we are not measuring yet.
- Exploratory Data. Data that is being captured, but that we don’t know a lot about yet (in a healthcare context). It is unclear what is good data vs bad data and what data is relevant. An example of data in this category is weather data. We collect a lot of it, but it is unknown today what the value of and impact that weather has on most healthcare operations (except maybe in extreme weather conditions). More work needs to be done here by scientists and statisticians before healthcare can operationalize data in this category.
- Known Data. Data in this category is known in healthcare and understood by specialized individuals in the organization: how it is defined, sourced, interpreted, and used. Radiology data is an example in this category.
- Well-understood data. This is data that is well understood by most people in healthcare. It is “foundational” data that has been integrated, consolidated, and already being leveraged by analytics/data warehouses. Claims data and billing data are examples.
Importance of this Data
“Enterprise Data Governance looks at all 4 of these areas,” commented Campbell. “But the focus is mostly on the bottom two layers. You need to be aware of the two top categories of data, but there really isn’t a lot you can do with data there right now. Work on the bottom two categories and you’ll put yourself in a strong position.”
Campbell believes that it is critical for healthcare organizations to invest in good data governance and management. Those that do, will be able to take advantage of new, exciting technologies like Artificial Intelligence (AI), machine learning, Natural Language Processing, chatbots, and predictive analytics. All of these are predicated on strong data foundation.
Without this, healthcare data stewards will be forced to spend time going back to the starting line each time they want to implement a new technology to clean and prep data. That can be extremely frustrating and repetitive. Better to invest in good data practices up front so that you can be ready for the next technology implementation.
How to Start
There are small things that healthcare CIOs and data stewards can do to get started on the “good data” journey. Campbell suggests:
- Audit existing reports. From experience, he estimates that upwards of 30% are not needed and another 20% can be consolidated. Ridding yourself of this technical debt can lighten the load when systems are changed or updated.
- Focus on data transparency. Invest the “elbow grease” to make your data processes transparent. Nothing erodes trust faster than data that “looks wrong”. Rather than let this doubt fester, you can deal with it immediately if it is clear how that data was sourced, consolidated, calculated, and reported on.
- Leverage tools and existing knowledge. Data governance is a team sport so don’t try to go it alone. Find people within the organization that can help. Leverage educational materials freely available online. Tap into tools like the Compendium Data Catalog for Healthcare (produced by DTA Healthcare Solutions)
“The good news is that you can tame your healthcare data,” concluded Campbell. “It’s not an impossible task. Just get started with a small initial step.”
Watch the full interview and learn:
- Why Average Length of Stay is an example of data with multiple definitions
- How trust is key to avoiding a data death spiral
- How data governance can be tied to an ROI
Learn more about DTA Healthcare Solutions: https://dtahealthcare.solutions/
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