How Quick Can We Analyze Health IT Data?

Posted on October 9, 2014 I Written By

John Lynn is the Founder of the blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of and John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

While at the AHIMA Annual convention, I had a chance to sit down with Dr. Jon Elion, President and CEO of ChartWise, where we had a really interesting discussion about healthcare data. You might remember this video interview of Dr. Elion that I did a few years back. He’s a smart man with some interesting insights.

In our discussion, Dr. Elion led me on an oft repeated data warehouse discussion that most data warehouses have data that’s a day (or more) old since most data warehouses batch their data load function nightly. While I think this is beginning to evolve, it’s still true for many data warehouses. There’s good reason why the export to a data warehouse needs to occur. An EHR system (or other IT system) is a transactional system that’s build on a transactional database. This makes it difficult to do really good data analysis. Thus the need to move the data from a transactional system to a data store designed for crunching data. Plus, most hospitals also combine data from a wide variety of systems into their data warehouse.

Dr. Elion then told me about how they’d worked hard to change this model and that their ChartWise system had been able to update a hospital’s data warehouse (I think they may call it something different) every 5 minutes. Think about how much more you can do with 5 minute old data than you can do with day old data. It makes a huge difference.

Data that’s this fresh becomes actionable data. A hospital’s risk management department could leverage this data to identify at risk patients that need a little extra attention. Unfortunately, if that data is a day old, it might be too late for you to be able to act and prevent the issue from getting worse. That’s just one simple example of how the fresh data can be analyzed and improve the care a patient receives. I’m sure you can come up with many others.

No doubt there are a bunch of other companies that are working to solve this problem as well. Certainly, day old healthcare data is valuable as well, but fresh data in your data warehouse is so much more actionable than day old data. I’m excited to see what really smart people will be able to do with all this fresh data in their data warehouse.