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The Medication List Said, “Raised toilet seat daily”

Posted on September 25, 2014 I Written By

The following is a guest blog post by Lisa Pike, CEO of Versio.
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With over a third of healthcare organizations switching to a new EHR in 2014, there is a lot of data movement going on. With the vast amount of effort it took to create that data, it’s a valuable asset to the organization. It can mean life or death; it can keep a hospital out of the courtroom; and it can mean the difference between a smooth-running organization and an operational nightmare.

But when that important data needs to be converted and moved to a new EHR, you realize just how complex it really is.

During a recent conversion of legacy data over to a new EHR, we came across this entry in the Medication List:  Raised toilet seat, daily.

Uh, come again??

How about this one?  “Dignity Plus XXL [adult diapers]; take one by mouth daily.”  What does the patient have, potty mouth?

Now, while we may snicker at the visual, it’s really no joke. These are actual entries encountered in source systems during clinical data migration projects. Some entries are comical; some are just odd; and some are downright frightening. But all of them are a conversion nightmare when you are migrating data.

Patient clinical data is unlike any other kind of data, for many reasons. It’s massive. It requires near-perfect accuracy. It’s also extremely complex, especially when you are not just migrating, but also converting from one system “language” to another.

Automated conversion is a common choice for healthcare organizations when moving data from legacy systems to newly adopted EHRs. It can be a great choice for some of the data, but not all. If your source says “hypertension, uncontrolled,” but your target system only has “uncontrolled hypertension,” that’s a simple enough inconsistency to overcome, but how would you predict every non-standard or incorrect entry you will encounter?

Here are some more actual examples. If you’re considering automated conversion, consider how your software would tangle up over these:

SOURCE SYSTEM SAYS COMMENTS
346.71D  Chm gr wo ara w nt wo st ???
levothyroxine 100 mg Should be mcg. Yikes!
Proventil Target system has 20 choices
NKDA (vomiting) NKDA= no known drug allergies.
Having no allergies causes vomiting?
Massage Therapy, take one by mouth twice weekly ???
Tylenol suppositories; take 1 by mouth daily Maybe not life-threatening, but certainly unpleasant
PMD
(Pelizaeus-Merzbacher disease)
Should have been PMDD
(premenstrual dysphoric disorder)
Allergy:  Reglan 5 mg Is patient allergic only to that dosage, or should this have been in the med list?
Confusing allergies and meds can be deadly.
Height 60 Centimeters or inches? Convert carefully!

 

These just scratch the surface of the myriad complexities, entry errors, and inconsistencies that exist in medical records across the industry. No matter how diligent your staff is, I guarantee your charts contain entries like these!

When an automated conversion program encounters data it can’t convert, it falls out as an “exception.” If the exception can’t be resolved, the data is simply left behind. Even with admirable effort, almost no one in the industry can capture more than 80% of the data. Some report as low as 50%.

How safe would you feel if your doctor didn’t know about 20% of your allergies? What if one of those left behind was the one that could kill you? What if a medication left behind was one you absolutely shouldn’t take with a new medication your doctor prescribed? Consider the woman whose aneurysm history was omitted during a conversion to a new EHR, so her specialist was unaware of it. She later died during a procedure when her aneurysm burst. I would say her family considered that data left behind pretty important, as did the treating physician, who could be found liable.

Liable, you say?

That’s right. The specialist could be found liable for the information in the legacy record because it was available….even if it was archived in an old EHR or paper chart.

You can begin to see the enormity of the problem and the potentially dangerous ramifications. Certainly every patient deserves an accurate record, and healthcare providers’ effectiveness, if not their very livelihood, depends on it. But maintaining the integrity of the data, especially during an EHR conversion, is no trivial task. Unfortunately, too many healthcare organizations underestimate it, and clearly it deserves more attention.

There is good news, however. With a well-planned conversion, using a system that combines robust technology with human expertise, it is possible to achieve 100% data capture with 99.8% accuracy. We’ve done it with well over a million patient chartsIt isn’t easy, but the results are worth it. Patients and doctors deserve no less.

Lisa Pike is the CEO of Versio, a healthcare technology company specializing in legacy data migration, with a proven track record of 100% data capture and 99.8% quality. We call it “No Data Left Behind.” For more information on Versio’s services or to schedule an introductory conversation, please visit us at www.MyVersio.com or email sales@myversio.com.

EHR Data Extraction and Clinical Conversion

Posted on July 5, 2011 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.

I think it’s quite easy to predict that 3-5 years from now, one of the top topics on this blog and in the EHR world as a whole is going to be around EHR data extraction or if you prefer EMR data conversion. I’ve previously predicted that by the end of the EHR stimulus money we’re be lucky to achieve 50% EHR adoption. So, you’d think that in 3-5 years we’d still be talking about EHR selection and implementation. Certainly, that will still be a topic of discussion. Not to mention, which EHR vendor they should go to for their second EHR. However, I am certain that 3-5 years from now we’re going to see a mass of doctors switching EHR vendors.

As part of my EHR blog week challenge (if you’re a blogger, you should participate too), today I’m going to highlight one of the foremost EHR professional and technical services company’s blog, Galen Healthcare Solutions which focuses on EHR data conversion.

I know I’ve written about EMR data conversion a number of times before. Although, I haven’t written about it much for quite a while. I guess meaningful use and the EHR incentive money has kind of dominated the conversation. However, there’s much that can and should be said about EHR data conversion.

The first thing anyone should know about EHR data conversion is that it’s not easy. In fact, it’s quite frankly an incredibly painful experience in almost every regard. Just take a look at this blog post summary of the EHR Clinical data conversion process by Justin Campbell of Galen Healthcare Solutions. He summarizes the steps as follows:
* Data Extraction
* Data Analysis: Cross-Referencing
* Design: Data Filtering, Matching (Provider, Patient Item), and Exceptions/Errors
* Testing
* Go-Live

I believe the most challenging item on this list is likely the Data Extraction. Sure, the data analysis and design are a pain to do and do well. However, the data extraction is often the most difficult part of an EHR data conversion, because you’re often working with an unfriendly EHR vendor that has lost you as a customer. Unfortunately, many EHR vendors haven’t heeded my call for EHR data independence, and so it can be a miserable experience trying to get the information and access you need to do an EHR data conversion. In some cases the EHR vendors will try and hold that data hostage.

The key for those selecting an EHR software is to be sure that the process for exporting your data from the EHR is part of your EHR contract. If it’s not, then add it to your contract. If they won’t add it to your contract, there are 300+ EHR vendors to choose from. Certainly it’s a part of the EHR contract that you hope to never have to use. Don’t take that risk.

Justin Campbell has also posted a few different data conversion success stories on the Galen Healthcare Solutions blog. Obviously, Galen has a lot of experience with the Allscripts Professional EHR software and so you’ll note this bias throughout the blog. However, the experience of the conversion is very interesting.

Here’s a paragraph from one of their data conversion success stories: Azalea Orthopedics.

To facilitate this conversion, flat-file extracts were obtained from MedManager for dictionaries, demographics and appointments. However, instead of using these extracts to import into Allscripts PM, an alternative approach was taken in which real-time appointment and demographic interfaces were deployed from the client’s existing Allscripts Enterprise EHR to the new Allscripts PM environment. This offered the flexibility of having the PM data populate real-time. Interfaces were also required from Allscripts PM to Allscripts Enterprise EHR. Thus as part of the go-live, existing reg/sched interfaces from MedManager to Allscripts Enterprise EHR needed to be deployed.

I have to admit that this kind of complexity in healthcare is what drives so many doctors nuts. I’m sure there were some functional reasons that they had to do all these interfaces between the systems. What I don’t understand is why the interfaces need to stay in place after the conversion is complete (at least if I understand it correctly). Did Galen really have to implement an interface between Allscripts PM and Allscripts Enterprise EHR? I’m sure there’s some long history for why this has to happen, but it’s such a terrible design. Certainly this isn’t Galen’s fault, but Allscripts. Interfaces are really great….when they work. When they don’t work, they drive a clinic, the IT person and even the EHR vendor absolutely nuts. I’ll be interested to learn more from Galen about why they did what they did.

I did find their report on the number of transactions processed fascinating:
Demographics: 156,900 processed in 491 minutes (8.18 hours)
Appointments; 313,280 processed in 1570 minutes (26.17 hours)

That’s a lot of data being processed. Can you imagine having to run the 26 hour data conversion twice if you messed it up the first time? Yep, data conversion is a tricky thing and can be very time consuming if you’re not really thorough in the process.

Imagine how much data will be collected 5 years from now with all these EHR implementations happening. Plus, the above data was only appointments and demographics. It doesn’t even include the physicians charting and other clinical data.