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Do Hospitals Need an EDW to Participate in an ACO?

Posted on July 29, 2013 I Written By

John Lynn is the Founder of the HealthcareScene.com 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 InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

The following is a guest blog post by Dana Sellers, Chief Executive Officer of Encore Health Resources. Dana’s comments are in response to my post titled, “Skinny Data Solves Specific Problems While BIG DATA Looks for Unseen Problems.” For more context, also check out my post on Skinny Data in Healthcare, and my video interview with Dana Sellers.
Dana-Sellers-Encore-Health-Resources
You did a great job of nailing down the kinds of problems our industry can tackle with BIG DATA on the one hand and smart, skinny data on the other in your blog last Thursday, “Skinny Data Solves Specific Problems While BIG DATA Looks for Unseen Problems.” We here at Encore Health Resources were particularly intrigued when you asked whether skinny data would be enough for ACOs, or whether hospitals will need full enterprise data warehouses – EDWs – to meet the demands of ACOs.

I’d love to take a shot at that. As I’m sure lots of your readers know, an EDW is a collection of enterprise data based on the best guess of what an organization thinks it will need over the long run. So it’s bigger than skinny data (only what we know we need now) but smaller than Big Data (every bit of data available). So now we get to your question…do hospitals need an EDW to meet the demands of participating in an ACO?

If you’ve got one, great! In large part, we know what measures ACOs want a hospital to report. If you already have a mature, well-populated EDW — fantastic! Pull the needed data, calculate the required measures, and go for it.

If not, start with skinny data. Many organizations find that they are jumping into ACOs before they have a mature EDW. So this is a great example of where skinny data is a great idea. The concept of skinny data lets you focus on the specific data required by the ACO. Instead of spending a long time trying to gather everything you might need eventually, focus on the immediate needs: quality, readmissions, unnecessary ED visits, controlling diabetes, controlling CHF, etc. Gather that quickly, and then build to a full EDW later.

Think about a skinny data appliance. One of the problems I’m seeing across the country is that organizations are rarely talking about just one ACO. These days, it’s multiple ACOs, and each one requires a different set of metrics. I talked with an organization last week that is abandoning its current business intelligence strategy and seeking a new one because they didn’t feel the old strategy was going to be able to accommodate the explosion of measures that are required by all the ACOs and commercial contracts and Federal initiatives coming down the road. The problem is that you don’t have to just report all these measures- you actually need to perform against these measures, or you won’t be reimbursed in this new world.

One way to deal with this is to establish a sound EDW strategy but supplement it with a skinny data appliance. I doubt that’s an official term, but my mother never told me I couldn’t make up words. To me, a skinny data appliance is something that sits on top of your EDW and gives you the ability to easily extract, manipulate, report, and monitor smaller subsets of data for a special purpose. As the demands of ACOs, commercial contracts, and Federal regulations proliferate, the ability to be quick and nimble will be critical — and being nimble without an army of programmers will be important. One large organization I know estimates that the use of a smart skinny data appliance may save them several FTEs (full time equivalents) per year, just in the programming of measures.

Bottom line – I believe skinny data will support current ACO requirements. Eventually, an EDW will be useful, and skinny data is a good way to get started. Many large organizations will go the EDW route, and they will benefit from a skinny data appliance.

John, as always, I love talking with you!

How Do You Improve the Quality of EHR Data for Healthcare Analytics?

Posted on May 8, 2013 I Written By

John Lynn is the Founder of the HealthcareScene.com 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 InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

A month or so ago I wrote a post comparing healthcare big data with skinny data. I was introduced to the concept of skinny data by Encore Health Resources at HIMSS. I absolutely love the idea of skinny data that provides meaningful results. I wish we could see more of it in healthcare.

However, I was also intrigued by something else that James Kouba, HIT Strategist at Encore Health Resources, told me during our discussion at HIMSS. James has a long background in doing big data in healthcare. He told me about a number of projects he’d worked on including full enterprise data warehouses for hospitals. Then, he described the challenge he’d faced on his previous healthcare data warehouse projects: quality data.

Anyone that’s participated in a healthcare data project won’t find the concept of quality data that intriguing. However, James then proceeded to tell me that he loved doing healthcare data projects with Encore Health Resources (largely a consulting company) because they could help improve the quality of the data.

When you think about the consulting services that Encore Health Resources and other consulting companies provide, they are well positioned to improve data quality. First, they know the data because they usually helped implement the EHR or other system that’s collecting the data. Second, they know how to change the systems that are collecting the data so that they’re collecting the right data. Third, these consultants are often much better at working with the end users to ensure they’re entering the data accurately. Most of the consultants have been end users before and so they know and often have a relationship with the end users. An EHR consultant’s discussion with an end user about data is very different than a big data analyst trying to convince the end user why data matters.

I found this to be a really unique opportunity for companies like Encore Health Resources. They can bridge the gap between medical workflows and data. Plus, if you’re focused on skinny data versus big data, then you know that all of the data you’re collecting is for a meaningful purpose.

I’d love to hear other methods you use to improve the quality of the EHR data. What have you seen work? Is the garbage in leads to garbage out the key to quality data? Many of the future healthcare IT innovations are going to come from the use of healthcare data. What can we do to make sure the healthcare data is worth using?