EHR, EMR, and Meaningful Use

Posted on May 20, 2009 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.

John over at EMR, EHR, and HIPAA wrote a great blog on meaningful use, and some of the definitions that are being kicked around in the healthcare IT world.  It is interesting to me that HIMSS includes in its definition of meaningful use ‘decision support.’

For a long time, my work revolved around decision support.  It’s truly an interesting area, and can include such suave topics as BI and decision support.  My biggest issue with it being a deciding factor in meaningful use is that it simply isn’t an option yet, especially for folks early in their adoption of the EHR.  

I’ve blogged about reporting off of an EHR before, and I will reiterate my point — it is vital that the reporting needs of an organization are explored during the EHR RFP process.   If the reporting needs aren’t understood when the EHR is adopted, chances are they won’t be met.   This also extends to decision support — just think of it as reporting needs on steroids.  Now, not only are you supporting one or two data points, you are supporting several, and attempting to allow the system to enact the 80/20 rule — that with 20% effort it could support 80% of the decisions.  

In order to support decision support (wow), it is necessary to have key data indicators in place, preferably in a discrete format (and not a bunch of free text) so that a few queries could be run, or a dashboard report created that will take care of it all.  If this doesn’t happen, I’ve seen quite a few clients turn to solutions that include SAS and SPSS for text mining to attempt to get at the same information.  Both SAS and SPSS are excellent tools for such work, and a little less unwieldy than attempting to write a query that includes a bunch of junk in the where clause because . . . Oh wait.  Let me go a bit slower.

If you end up having a lot of free text data that you have to report off of, you have to remember this little accidental thing called a typo.  One of the first text mining projects that I worked on involved data that came from an insurance call center, and “Payment” was spelled in over 75 different ways (including typos). Don’t believe me?










To write a SQL query that would be able to SORT OF predict if a person meant payment would have a query that would be very, very slow to run.  Add on the volume that your clinic has, and in a few years it could be quite a mess.  All of this can be taken care of if you simply address the needs from the beginning, and start to watch regulations and motions in congress to understand what upcoming data needs for things like HEDIS, PQRI, and Pay for Performance would be.   To make matters worse, there are still often ‘fixes’ you have to put in the data in order to make it fit into some reporting needs, and ultimately decision support.

My problem with ‘meaningful use’ including decision support is the fact that it will sting early adopters.  So, if you had adopted an EHR in, say, 2002, you might not have seen all of these issues coming — and your EHR may not include robust decision support, or have strong data structures to support it.  I would hope that if you adopted your EHR in 2002 that much care has gone into it, but I have also seen EHR’s implemented as late as 2006 that contain unruly data.  I tend to find that in the larger the clinic and the more the users, the more of a problem this becomes.

Yes, there are ways to retroactively fit an EHR to support decision support, but it involves a lot of groundwork.  You have to establish how far you will want to go back, you’ll have to understand the amount and quality of data at every single step, and you will have to somehow code around that data.  You have to decide if you want to retroactively fit that data back into the medical record, or if you want to introduce a data warehouse to hold it (a project that is both expensive and very likely to have issues along the way, depending on what systems are included).

I do believe that decision support is a vital function of the modern EHR.  I’ve seen some that really assist a physician in performing their tasks, yet were rejected in an RFP phase because they would not support reporting requirements such as HEDIS or OSHPD, or even the operational and financial statistics of the department.  What I’m not sure about it is how they expect to legislate this.  Are they going to come into clinics and run our data to see?  As a big privacy advocate, I can’t say I like that decision.  But I’m also not keen on ‘self reporting.’

I worry about organizations that will try to short cut their decision support by just ‘using the billing data.’  Billing data needs to be created in tandem with clinical data, but it functions under different rules, and has different requirements.  The skew that using billing data with no clinical context will not be true decision support — it will be merely meeting the letter and not the spirit of the law.  How will legislation address this?

I am wondering how they are going to legislate this, given that at quite a few of the clients I have worked with, their only recourse is running reports, and getting back to the department that is acting like an “EHR Rogue.”  How will Congress decide who is meaningfully using systems?  How can this possibly be regulated at a federal level, when we all have problems regulating it ourselves?

Food for thought, for sure.