Last year, as ICD-10 implementation approached, organizations throughout the U.S. reported varying levels of comfort with regard to readiness and understanding of the impact of ICD-10 on physician workflow. For some, it was business as usual. For other physicians, ICD-10 became one more check box on the list of reasons to leave practice.
Accurate patient matching within the EMR should not be a concern limited to HIM professionals. Ensuring that medical record data is correct and complete and that duplicate records are not created is key to various healthcare initiatives, including population health management, analytics, information governance, patient-centric care, health information exchanges, and finance. It all starts with the patient's record. Reducing the number of duplicate records at a hospital and being able to effectively match records is critical to ensuring that these healthcare initiatives are successful, says Lesley Kadlec, MA, RHIA, CHDA, director of HIM practice excellence for AHIMA.
"Patient matching is really the underpinning of all the strategic initiatives that are going on in healthcare," Kadlec says. "You have to have accurate patient information to have accurate patient care. Ensuring that you have the right patient and the right information at the right time is really what drives the physicians' and clinicians' ability to actually provide that patient with care."
More than half of HIM professionals work with mitigating duplicate patient records, and of that group, 72% do so on a weekly basis, according to a recent survey of AHIMA members. Unfortunately, less than half of all respondents have quality assurance in place for their registration or post-registration processes. (A summary of the data is available in the Journal of AHIMA.)
"The challenge is having the staff to be able to dedicate to making the corrections, doing the matching, and ensuring that everything is getting put back together," Kadlec says.
Patient matching and duplicate records are a major issue right now because hospitals are using so many different systems and there is often a lack of information governance across those systems, says Megan Munns, RHIA, identity manager at Just Associates, Inc., based in Denver.
Few in the healthcare industry would argue that the way the government currently pays for drugs is the most cost-effective, efficient, and equitable method possible.
Our readers have been asking for an updated medical record documentation guide, and here it is?new and improved! The guide provides references to the associated CMS Conditions of Participation and new and revised standards and elements of performance (EP). A recent Joint Commission column discussed ongoing record reviews and the continued focus of Joint Commission surveyors related to documentation in the medical record. The guide takes the Record of Care, Treatment, and Services chapter and breaks it down into an easy-to-use tool for comprehensive record reviews by topic.
Do you recall the recent humorous television commercial for phone services that featured children who wanted more and tried to explain why? The core message was that more isn't always better. I believe there are many applications of this principle in healthcare. To understand why this is the case, since large evolves from small, you might have to engage your sense of recall to visualize the past compared to the present. We'll look at some examples below.
Big (bad?) data
For all the talk about population health and big data, there is less discussion about data integrity, a key principle in data usage. Anyone who has worked with the most basic of databases, the master patient index, knows how many errors occur in collecting up-front patient access data. Errors still abound in duplicate medical record and account data. How can any of the data associated with these accounts be considered valid and worthy of basing conclusions upon? How confident are we, really, in our interpretation of this data?
For example, comparative MedPAR data will not display ICD-10-CM/PCS data until at least 18 months after ICD-10 implementation. There is no way to measure if we are undercoding, overcoding, erroneously coding, or problematically grouping any cases until we have enough data to make some judgments. Even then, the only true audit is one that compares the collected data with the source documents (in this case, the medical record). Organizations must conduct multiple rounds of these audits before findings can even be discussed.
The best approach is to begin your own audit of small segments (e.g., most common, most at risk) of diagnoses and procedures rather than waiting until the MedPAR data arrives. Be aware that if you are looking at any comparison databases, there is likely a crudely mapped comparison going on between ICD-9 databases (and ICD-10). As we all know, comparisons are not possible in all cases, and the more cross-mapping we do, the less granularly correct the comparison outcome data is, which decreases the validity of the universe of data.
In HIM, there are other data quality issues that have an unknown impact on integrity comparisons. For example, are we comparing apples to apples for sites that are using computer-assisted coding applications versus those that are not? Is it fair to compare outsourced coding with in-house coding? In a recent study conducted for a client, I observed that the time for coding of outsourced cases was dropping in a direct ratio to the case mix. Are we gaining productivity but sacrificing quality and reimbursement potential?
