Population Health vendors are experts in predictive data models, risk stratification, data warehousing, reporting and a long list of other sophisticated technologies; patient matching isn’t one of them.
Tag: Patient Matching
Health insurance companies, health systems and other tech companies seem to be finally getting comfortable with debunking a lot of myths around why IT teams should consider moving infrastructure to the cloud.
While biometrics are a key piece of the puzzle in solving our nation’s challenges in identifying patients and linking health data to the correct patients, they are still just one piece of the puzzle.
Considering a cloud-based EMPI, but unsure whether to use a hosted EMPI, a managed EMPI, or a software-as-a-service (SaaS) EMPI? Learn the benefits and challenges of each.
Read this blog post to learn what patient matching is and why it is important, as well as what Referential Matching technology is and how it can help solve our nation’s interoperability challenges.
Last week, I got the chance to join the HIE community in Atlanta for the 2018 SHIEC Annual Conference. This is my third straight year attending, and since Verato has 15 HIE customers, SHIEC is always a particularly fun event for us. Here are five big takeaways from this year’s conference.
All conventional patient matching technologies are fundamentally the same, and fundamentally limited. Referential Matching is a fundamentally 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.
I come from a big family, so I am not the only one to suffer from the St. Thomas-specific variety of patient matching woes. While we share our war stories often (only half of us have been able to finagle a period into our last names on social media, and we have all spent hours on the phone with various customer service lines), there is one anecdote that is always the first to come to mind.
This weekend will be filled with thrilling sports matches, including the World Cup Final on Sunday. Here’s to hoping bad data doesn’t influence the match.
Healthcare’s focus on the triple aim has spawned thousands of initiatives like improving the patient experience, measuring quality and performance, and enterprise clinical integration. But each of these initiatives shares a foundational assumption that frankly should be challenged: they assume access to patient data that is uniquely identified and correctly matched.
As the largest Integrated Health System in the United State, the Veterans Health Administration (VHA) gets a lot of attention for every step it might make. Existing MPI technologies cannot resolve patient identities well enough to support VHA. But our new patient identity resolution technology, the Verato Universal MPI, could support the new needs.
No matter how much you have invested in your EHR or enterprise master patient index (EMPI) technology, no matter how small your patient population is, and no matter how diligent your registration staff is, your EHR or EMPI is riddled with duplicate patient records, which have three hidden costs.
Death and Taxes. We have all heard the inescapable phrase that binds us under a single truism. Mergers and Acquisitions (M&A) are another unavoidable event that we should care about, and Referential Matching technology can help solve patient matching and identity resolution challenges during M&A activity.
The “Laurel-Yanny” illusion got me thinking about the healthcare industry’s patient matching challenges, and how Referential Matching technology can help.
With a period and a space in my last name, I’ve experienced more than my fair share of duplicate records, misidentification, and the struggles of patient matching.
We wrote a letter requesting that as part of their analysis and recommendation, the GAO consider “Referential Matching” as an alternative solution to existing patient matching technologies that have failed our healthcare system.
Problems with patient matching underlie many of the more visible portability issues a patient may see from their physician such as receiving incomplete medical records. Luckily, Referential Matching technology is a powerful solution.
Announcing the first ever tech lab dedicated to improving patient matching using blockchain, the technology that’s revolutionizing healthcare.
It’s taken a couple weeks for things to settle post-HIMSS and we are still invigorated from all of the energy, activity, and innovation we witnessed. Most important to note, from our perspective, was the focus on interoperability as a national priority.
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 EHR or EMPI, and that they can meet the ONC mandate of a having a 0.5% duplicate rate by 2020.
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.
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.
Payers are grappling an increasingly prevalent problem: how to match disparate data to the right person. Referential Matching technology is well-suited to help.
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 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 Referential Matching plug-in that works continuously to find and remediate duplicates an EHR or EMPI has missed, and that 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.