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

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?

About the author

John Lynn

John Lynn is the Founder of HealthcareScene.com, a network of leading Healthcare IT resources. The flagship blog, Healthcare IT Today, contains over 13,000 articles with over half of the articles written by John. These EMR and Healthcare IT related articles have been viewed over 20 million times.

John manages Healthcare IT Central, the leading career Health IT job board. He also organizes the first of its kind conference and community focused on healthcare marketing, Healthcare and IT Marketing Conference, and a healthcare IT conference, EXPO.health, focused on practical healthcare IT innovation. John is an advisor to multiple healthcare IT companies. John is highly involved in social media, and in addition to his blogs can be found on Twitter: @techguy.

10 Comments

  • Great topic, John! There are several ways to improve the quality and completeness of the EHR data! Absolutely, knowledgable consultants can assist. Before you even get to the system, though, it’s all about business process: documenting the TRUE current state of each process, identifying “broken” processes which may lead to missing or inaccurate data, then establishing correct business processes for data entry will improve the quality of ANY EMR system. There are also data quality controls built into the EMRs, themselves, at the system level – these vary by vendor, system, version of the system, etc.

    I’m a data nut, but I dream of a world where the process portion of the equation is addressed each and every time. Too often, we in IT are told to configure systems to meet broken process requirements – and the quality of the data and resultant analytics are bound to suffer.

  • Mandi,
    Good comment. In fact, it reinforces my post, because the best EHR consultants focus on the business process as much as the software itself. Certainly some EHRs have some quality control mechanisms they can leverage as well, but most of it is process.

  • John,

    Actually, I’m not interested in improving the quality of EHR for healthcare analytics because that’s not the purpose of an EHR and until EHR developers understand this and start developing the EHR as a form of communication from one professional to another about the care of the patient they will never get usable data from them.

  • I agree Mandi – this is a great topic, and one that doesn’t get as much attention in healthcare as it needs. As noted, there are systems that have some controls built in, but even so there is no guarantee that quality data will result. For example, can any EMR guarantee that every field included in an HL7 transaction will be populated with data? Or with data that has the same meaning to the recipient of the transaction as to the sender? Probably not.

    Mandi’s statement that understanding business process is critical to ensuring good data is very true. It is also true that there needs to be a level of uniformity and governance regarding how the data captured and stored within EMRs (and other healthcare applications as well) is defined and utilized. Consultants familiar with EMRs can help to facilitate this, but that’s only part of the story.

    There is a much broader picture here with regards to healthcare data, one that goes beyond the business processes surrounding the EMR/EHRs and encompasses the entire lifecycle of healthcare data — starting from the patient and branching out in the many different directions (payer, provider, facility, researcher, analyst, etc.) Consultants need to be able to at least begin to understand that broader picture to be able to help get the right data quality processes embedded into each of the various branches.

    I, too, am a data geek and am hoping that someday that we all reach the Emerald City in regards to ensuring better data quality in healthcare.

  • @ James – Your response is very much what was running through my mind while reading this post.

    Data geeks beware – docs don’t care about you.

    This goes back to the basic argument of: if an EHR actually helped docs run more efficiently, they’d all have switched, on their own, years ago. Plus, since they love using the software, you’d find data quality to be much better.

    But instead, docs are pissed that they are pushed to buy software and hardware, and don’t want to learn the software.

    Meaningful use does nothing to help the quality of data, as docs generally min-run everything to do with MU in order to get that first $18,000 check.

    Until the dynamic changes from being forced to use an EHR to wanting to use an EHR, data quality is going to suffer.

    I’m sure even before EHRs were part of the mix, the huge amounts of medicare/medicaid fraud have made health data quality suffer also.

  • I believe healthcare data should be about improving outcomes for patient care. I am incorporating those metrics into our EMR that I am designing with our team. Anyone interested please contact me. I have submitted a proposal to our state regarding this.

  • John,
    Your comment is interesting as many doctors are selling their practice to large hospital systems. By doing so, they start to become beholden to the larger entity and the large entity cares about the data. The doctor will have to start caring if they continue to work for the hospital system. Of course, they could leave and start that cycle over again.

  • I agree with the premise of the posts. Power to change outcomes starts with liberating the data. Then transforming all that data into information and finally into knowledge. Ok – Sorry, that’s probably blindingly obvious. But skinny-data is a good metaphor because you don’t need to liberate ALL the data. And in fact the skinny metaphor covers what I refer to as the data becoming information part (filter out the noise). Selective liberation and combination into a skinny warehouse or skinny data platform is also manageable. And then build on top of that the anlytics that release the knowledge to enable better outcomes. Now …if only all those behometh mandated products would loosen up on their data controls…

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