Is Referential Matching the Answer to Duplicate EMRs?
What is referential matching? Is it:
a.) A form of online dating?
b.) A way to bring together a reference librarian and a reader?
c.) A way to organize partners for a tennis game?
d.) A way to bring together all pieces of patient’s medical information, or to connect health data to the correct patient?
If you answered d), you’re well on your way to understanding this important new development in health information management (HIM).
Sometimes, More is Less
If you use an electronic medical record (EMR) for a hospital or health system, you know all too well the problems associated with them. A patient receives treatment for a condition, and that patient’s demographic information is correctly entered into your system. Sometime later, that same patient receives treatment for another condition. In between treatments, the patient moves, or gets married and changes their last name. That new information gets entered into the EMR, and a duplicate record is born.
Needless to say, that’s a big problem, as it can create billing problems and, worse, compromise patient care. A report on patient misidentification published by Patient Safety & Quality Healthcare (PSQH) estimates:
- “86% of respondents have witnessed a medical error as the direct result of misidentification; and
- “35% of all denied claims are due to misidentification, which can cost hospitals up to $1.2 million a year.”
These numbers quickly add up to some big headaches for any institution.
What’s the Solution?
In most EMRs, a built-in module, known as a master patient index (MPI), uses an algorithm to look for records that may possibly belong to the same patient. The module highlights records that contain some identical demographic identifiers, but which may have minor differences in, say, the way a person’s name or a street name has been spelled. The records can then be examined and reconciled.
A similar process can be employed across EMRs in larger institutions with multiple facilities or units — or even different kinds of EMRs — using more sophisticated MPI, known as an enterprise master patient index (EMPI).
But MPIs and EMPIs are only helpful to a point. Some kinds of records that contain outdated data (such as the example above, where the patient has changed their name and address) are difficult for them to find. Records with missing or incomplete data will also go undetected.
That’s where referential matching takes over.
The Advantage of Referential Matching
Instead of matching records within an EMR or across a system, referential matching allows record comparison with patient information in a HIPAA-compliant database that is continuously maintained and updated. Referential matching can simply be plugged into an individual EMR or an organization-wide EMPI, no matter what the vendor platform, to identify potentially duplicate records.
Referential matching also can automatically resolve duplication errors within and across EMRs. And it can provide insights into how data entry is creating the duplicate entries to begin with.
That makes referential matching a potentially powerful new tool for your institution to use in its ongoing efforts to create clean, accurate records — although, it’s probably not quite so much fun as online dating.
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