I was asked to represent Verato during the Strategic Health Information Exchange Collaborative (SHIEC) conference held in Indianapolis. The conference was thriving with ideas to improve patient care, patient experience, and overall outcomes. Topics on Patient Centered Data Home (PCDH), Prescription Drug Monitoring Program (PDMP), Interoperability, and Behavioral Health were some of my favorites, perhaps because their use cases depend tremendously on accurate identity resolution.
Tag: Patient Matching
One of the biggest challenges in patient identification is the ability to tie together all of a patient’s information and accurately associate it with that patient. This is where Verato comes in. Our approach is called “referential matching” and the Verato Universal MPI is the only master patient index to use it
Today, we mourn the timely passing of a dear but increasingly obsolete—and obstinate—friend, Master Patient Index.
As the healthcare industry continues to push toward interoperability, one key component is still lagging, causing cracks in the foundation of any successful data framework: Patient identity matching.
IBM Initiate is a state-of-the-art master patient index (MPI) solution with best-of-breed patient matching capabilities. But it requires thousands or millions of potential matches to be manually reviewed. Learn how you can automatically match those potential matches, saving the time and effort of manual review.
It’s time for organizations to stop performing MPI cleanups. Verato offers a subscription service that lets organizations prevent duplicates from being created in the first place.
AHIMA16 – Stop Cleaning Your Identity Data! Achieve Interoperability of Patient Information Despite Dirty and Out-of-Date Data
Healthcare organizations perform large data quality exercises and enforce strict data governance standards in order to better match patient identities and improve interoperability. But there is a new way to match patient identities despite low quality data.
Traditional patient matching engines use patients’ names, addresses, and other identity data to link patient records together. But this data is constantly changing, making matching a challenge. This blog examines how you can accurately match patient records even if they contain out-of-date data.