By using Verato Auto-Steward and its Referential Matching approach, organizations can ensure that they achieve the original business case established for the use of their EMR or EMPI, and that they can meet the ONC mandate of a having a 0.5% duplicate rate by 2020.
Tag: Patient Matching
Verato pens letter to U.S. Senate and House recommending Referential Matching as nationwide strategy
Verato wrote a letter to Senate and House committees urging them to consider a fundamentally different approach to patient matching – called Referential Matching – that will finally enable an accurate, secure, and scalable nationwide patient matching strategy. Read the full contents of the letter here.
Well it’s that time of year. The holidays are here, and we’ve been trying to think of clever holiday puns to catch your attention. The best we could come up with was writing a year-end “wrap up,” which you can correctly assume is a reference to wrapping presents. I know, it’s hilarious. Anyway, if you… Read more »
Read about three steps that health systems, payers, and health information exchanges (HIEs) like yours can take to crawl, then walk, then run to lower duplicate rates and improved patient matching
A few years ago, patient matching was a challenge addressed by health information management professionals within the four walls of their hospitals and health systems. Today, accurate patient matching has become a national imperative—in early October, five US senators wrote a letter to the GAO urging it to consider the effects of a national patient matching strategy.
As technology continues to embed itself into our daily lives, we all create more and more data pertinent to our health – through telemedicine, patient portals, medical devices, behavioral health. Even our socioeconomic information is increasingly – and correctly – being treated as health data. With more information available than ever before, shouldn’t payers and… Read more »
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.
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.