It’s official: spring is here. I know this because today three of the men in our HQ offices coincidentally wore shirts of varying shades of pink. That only ever seems to happen in the spring.
At lunch, with all three shirts sitting around the same table in our kitchen, some of my coworkers got into a rousing discussion as to what the colors actually were. One was certainly more of a reddish pink, while another was more of a pinkish red. The third had some noticeable inclinations toward orange. What some called “salmon” others insisted was “coral.” One coworker thought two were simply “pink” while the third was an exotic “burnt sienna.” Another suggested they were all “red,” which was laughably incorrect — he left in a huff abruptly after finishing his sandwich.
As the debate escalated, everyone pulled their phones out. Everyone searched for examples of “coral” and “salmon” and “crimson” and “scarlet.” Everyone showed everyone else a picture they thought definitively proved their correctness. Everyone held their phone up to each shirt in sequence and said, “See? Identical.”
Our only salvation was an obscure webpage someone eventually stumbled across that explicitly addressed the nuanced differences in shades of red and pink. Together, we scrolled through hundreds of color swatches, each labeled with its corresponding color name — which ran from the mundane (“red”) to classic (“fire engine”) to ridiculous (“blooming dahlia”).
Finally, we’d found an answer key for our color queries — a single, comprehensive source of truth we could all look to in this time of crisis. Rather than comparing two shirt colors to each other to see if they were the same, we could compare them to this answer key. If both shirts matched the “coral” swatch, we could be certain they matched each other.
After lunch — and once I’d had a chance to cool down, collect my thoughts, and make amends with my coworkers — I couldn’t help but think of how this story could act as a parable for patient matching.
Many healthcare organizations employ teams of data stewards and health information managers to manually review pairs of patient records that are potential duplicates. These are records that the organization’s patient matching technology (typically its EHR or an enterprise master patient index technology, or EMPI) could not definitively match due to differences in demographic data. For example, two records might share an SSN and first name, but have a different last name and address — a common pattern that often occurs when someone gets married. These records are close enough to be considered a potential duplicate, but not so close that the EHR or EMPI will automatically match them. Instead, the EHR or EMPI will flag them and create a “task” for a data steward or HIM professional to manually review the records to make a determination. This is the patient matching equivalent of my coworkers' manually intensive and subjective determinations of whether two shirts matched or not.
As many as 30% of a health system’s patient records will be flagged as "potential duplicate records" by the EHR or EMPI. And these tasks are often created faster than HIM staff can keep up, resulting in a growing backlog of duplicates awaiting resolution.
But the risks and costs of duplicate records are skyrocketing. According to a Black Book survey, every duplicate record costs healthcare organizations over $800 per emergency department (ED) visit, and over $1,950 per inpatient stay due to redundant medical tests and procedures. In addition to these costs, the same survey found that 33% of all denied claims were a result of poor patient matching, costing the healthcare industry $2 billion annually. And all of these duplicate records reflect poorly on healthcare organizations — 88% of consumers directly blame the hospital system for their dissatisfaction with the lack of portability of their health care records.
Luckily, there is a quick and easy plug-in for EHR and EMPI technologies that can automatically find and resolve those technologies' missed matches and duplicate records. This plug-in, called Verato Auto-Steward™, can even automatically resolve the "potential duplicate records" the EHR or EMPI has flagged for manual resolution by HIM staff or data stewards.
Verato Auto-Steward leverages a powerful new patient matching technology called “Referential Matching” to automatically find and resolve matches and duplicate records that conventional patient matching technologies never could.
Rather than directly comparing the demographic data from two records to make a match, Verato Auto-Steward compares the data from those records to Verato's comprehensive and continuously-updated reference database. This database contains over 300 million identities spanning the entire U.S. population, and each identity contains a complete demographic data profile spanning a 30-year history — including nicknames, aliases, maiden names, common typos, past phone numbers, and old addresses. This database is essentially an “answer key” for patient demographic data, and Verato references this answer key during the matching process to match records even if they contain demographic data that is out-of-date, incomplete, incorrect, or different.
A universal answer key that enables better match decisions... sound familiar?