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Gold Standard. Silver Bullet.

Thought Leadership

Summary: All conventional patient matching technologies are fundamentally the same, and fundamentally limited. Referential Matching is a completely different approach to patient matching. It is the new gold standard in patient matching technology, and it is the silver bullet many organizations seek to solve their patient matching challenges.

All conventional patient matching technologies are fundamentally the same

No matter what patient matching technology you use, and no matter what vendor it is from, it uses fundamentally the same approach to patient matching as every other technology. This is true whether you use the built-in master patient index (MPI) module in your EHR from Epic®, Cerner®, or another vendor, or whether you use an enterprise master patient index (EMPI) product from a vendor like IBM®, NextGate®, or QuadraMed®.

This is because all conventional patient matching technologies use algorithms to compare the demographic data from two patient records to determine if those records match—in other words, if they belong to the same person. If the demographic data is the same or very close, the technology determines that the records match.

The most sophisticated of these algorithms are called “probabilistic” algorithms, and they use statistics, weights, thresholds, rules, and complicated math to calculate the probability that two patient records match. This lets them overcome minor data errors like misspelled names and mistyped birthdates. And it lets them understand that two records with the last name “Rumpelstiltskin” are more likely to belong to the same person than two with the last name “Smith.”

Probabilistic algorithms have actually been around since the 1970s, but they have seen little innovation since then. Yet they are the cornerstone technology of almost every MPI and EMPI on the market—even though they often masquerade under names like “vector-based matching” or “fuzzy matching” to appear new or differentiated.

All conventional patient matching technologies are fundamentally limited

Because probabilistic algorithms directly compare the demographic data from two records, their accuracy is fundamentally limited by the quality and completeness of the underlying patient demographic data they are comparing. Yet patient demographic data is of notoriously low quality and completeness. In fact, typically 30% of patient demographic data in an organization’s systems is out-of-date, incomplete, or errored—making patient matching extremely challenging for even the most sophisticated probabilistic algorithms.

Consider these examples of matches that no algorithmic approach—no matter how advanced—could ever make:

  • One record contains a patient’s old address and maiden name, and another contains a patient’s current address and married name
  • One record contains very sparse demographic data, such as just a patient’s name and birthdate
  • One record contains a patient’s name, address, and SSN, while another contains a patient’s name, phone number, and birthdate

Because of their fundamental limitation, the probabilistic algorithms found in conventional patient matching technologies typically miss 10-20% of matches—leaving EHRs riddled with duplicate records, preventing organizations from assembling complete patient histories, and leading to massive and costly inefficiencies in the revenue cycle.

To compensate, organizations are forced to invest in data quality initiatives, data cleanup exercises, data governance committees, and data stewardship efforts whereby health information management (HIM) staff manually review and resolve records their EHR or EMPI cannot definitively match but has still flagged as “potential duplicates.”

Referential Matching technology is a completely different approach

Verato has pioneered a powerful new patient matching technology called “Referential Matching.” Rather than directly comparing the demographic data from two patient records to see if they match, Verato instead matches that demographic data to its comprehensive and continuously-updated reference database of identities. This proprietary database contains over 300 million identities spanning the entire U.S. population, and each identity contains a complete profile of demographic data spanning a 30-year history. It is essentially a pre-built answer key for all patient demographic data.

By matching records to this database instead of to each other, Verato can make matches that conventional patient matching technologies could never make—even patient records containing demographic data that is out-of-date, incomplete, incorrect, or different.

Referential Matching technology is so accurate and so powerful that providers, payers, and HIEs across the country are using it to improve the patient matching of their EHR and EMPI technologies—by using a Referential Matching plug-in to automatically find and resolve missed matches and duplicate records in their EHR or EMPI, including automatically resolving the “potential duplicate records” that the EHR or EMPI has flagged for manual resolution by HIM staff.

Referential Matching technology is the new gold standard in patient matching

Referential Matching isn’t simply a better algorithm—it is a completely new approach that represents a quantum leap in patient matching technology and accuracy. It combines sophisticated probabilistic algorithms with big data and machine learning technologies, and it incorporates 50,000 person-hours of data science and engineering efforts to ensure the integrity and completeness of the reference data. And it does all of this in a highly scalable and secure cloud infrastructure—allowing any organization to instantly and dramatically improve its patient matching through simple integrations and modern APIs.

Simply put, Referential Matching technology is the new gold standard in patient matching.

Referential Matching technology is the silver bullet for your patient matching challenges

Healthcare organizations often have complicated processes and intricately integrated technology ecosystems in order to have just-good-enough patient matching. And they often have teams of HIM staff working through thousands or millions of “potential duplicate record” tasks that their EHR or EMPI has flagged for manual resolution. Yet despite all of this cost and effort, on average 18% of a health system’s patient records are duplicates1, and health systems suffer the massive costs of these duplicate records. In fact, every duplicate record costs providers $800 per ED visit and $1,950 per inpatient stay2, and inaccurate patient matching costs providers $17.4M annually in denied claims3.

Because of these high costs, and because of the repercussions that inaccurate patient matching has on patient safety and quality of care, healthcare organizations are seeking a silver bullet—a magical new technology that can instantly improve their patient matching without disrupting any of their complicated processes or their intricately integrated technology ecosystems.

Luckily, Verato offers two such silver bullets—two cloud-based solutions powered by Referential Matching technology.

  1. Verato Auto-Steward™ is a simple cloud-based plug-in for your EHR, EMPI, or MDM technology that automatically finds and resolves its missed matches and duplicate records using Referential Matching. Verato Auto-Steward can even automatically resolve the “potential duplicate records” your EHR, EMPI, or MDM has flagged for manual resolution by HIM staff or data stewards.
  2. The Verato Universal™ MPI is a HITRUST-certified SaaS solution that uses Referential Matching technology to match and link your patient or member records across your enterprise with the highest accuracy rates in the industry. Simply put, the Verato Universal MPI is the most accurate, most secure, easiest to implement, and most cost effective EMPI solution on the market—and you can deploy it in as little as six weeks.

[1] 2018 Mid-Year EHR Consumer Satisfaction Survey, Black Book Market Research
[2] 2018 Mid-Year EHR Consumer Satisfaction Survey, Black Book Market Research
[3] 2016 National Patient Misidentification Report, Ponemon Institute