Verato® solutions are powered by our industry-leading Verato MDM Cloud™, the next-generation master data management (MDM) used by healthcare organizations to ensure accurate and complete person matching. Whether you manage data for patients, members, consumers, or providers, our integrated technology delivers highly precise identity resolution — giving you a trusted, 360-degree view of every individual.

What is an enterprise master patient index?

An enterprise master patient index (EMPI) is a healthcare identity system that links and reconciles records for the same individual across multiple applications, ensuring organizations maintain accurate, complete, and consistent person data. Unlike basic MPIs that match records within a single system, an EMPI connects identities across electronic health records (EHRs), CRMs, claims systems, and analytics platforms, supporting a unified, trustworthy view of each person.

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What is an EMPI used for?

Healthcare organizations use an EMPI to reliably match and connect identity data across siloed systems, including EMRs, CRMs, public health databases, and payer platforms. Because so much healthcare information depends on accurate identity, an EMPI is essential for managing data across a complex ecosystem.

What is the difference between MPI and EMPI software?

Master patient index (MPI) systems match records within a single application using basic algorithms. Enterprise master patient index (EMPI) systems match records across multiple applications using more advanced logic, providing a broader, more complete view of an individual.

How does an EMPI work?

An EMPI links identity records by assigning each individual a unique identifier and connecting their information across systems using patient matching algorithms.

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Patient matching algorithms

Patient matching compares demographic data to determine if two records belong to one person. Errors, missing information, and inconsistencies make this process challenging.

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The Verato difference: Native referential matching

Verato combines probabilistic algorithms with a curated demographic reference database to dramatically improve matching accuracy — even when source data is flawed.

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