How to evolve today's patient matching technologies

If traditional patient matching technologies are failing you, here’s how to use a multifaceted approach to achieve higher matching rates and mitigate matching challenges associated with data quality.

It’s estimated that around 30% of patient data held in electronic health records is incomplete or inaccurate, and up to half of all patient records may not be linked correctly. Unreliable patient data presents huge problems for the healthcare industry, from flawed diagnoses and treatment errors to unreliable analytics, and billing mistakes. Much of the work done to resolve patient identities relies on manual fixes that continue to drive up the cost of care.

With patients flowing through multiple systems and interacting with numerous clinicians and staff, combined with constantly changing patient demographics (e.g., name or address changes), how can mismatched patient records be prevented?

Here’s a look at how we can match and connect disparate patient records to give a more accurate and complete picture of patients.