The annual HIMSS conference is typically a flurry of new product announcements, which in turn leads to a lot of excitement about how technology will push healthcare to be more collaborative, cooperative and – hopefully – affordable.
One of the biggest splashes of this year was Apple’s announcement of a new Personal Health Record (PHR) – called Apple Health Records – which would allow users to aggregate their existing patient-generated data in the Health app with the data from their physicians’ EHRs, if those EHRs exist in a participating health system. This is an exciting step forward to giving patients control of their data, and most importantly may help proactive patients to improve their health by giving them easier visibility into their medical records.
Apple’s announcement is a prime example of the value of health record portability. Consumers – especially as they are collectively becoming more tech-savvy – are beginning to expect such portability. Interestingly, a recent Black Book market survey surfaced an important insight: whenever there are barriers to health record portability, patients blame their doctor – not the technology. As patients increasingly pay more for care, they are also shopping more for services, and their satisfaction will certainly affect healthcare organizations’ bottom lines.
While some of that blame may be well placed on physicians, the technology enabling portability of health records has not kept up with the industry’s desires. One of the foundational issues behind health record exchange is when technology is unable to answer a most basic question: are we talking about the same patient?
Problems with patient matching – or being able to accurately answer that “same patient?” question – underlie many of the more visible portability issues a patient may see from their physician such as receiving incomplete medical records or going into registration and having an office unable to find their medical record at all.
Conventional patient matching approaches rely on demographic data that often changes, leaving records full of incorrect or out-of-date information. Name changes, address changes, phone number or email address updates all contribute to this problem, yet most of us will experience them. And these data errors compound with more data sources, compounding even more as technology platforms work to integrate data such as an EHR and a PHR.
The simple fact is that no matter how expensive your EHR is, no matter what size your patient population is, no matter how clean your data is, and no matter how diligent your registration staff is, your EHR is riddled with duplicate records. In fact, according to that same recent Black Book survey, the average duplicate rate across healthcare organizations is 18%.
The Apple PHR and other PHR applications like them are exposed to those issues lurking in healthcare organizations’ duplicate rates. They will need to urge healthcare organizations to solve their internal patient matching problems or Apple will fail to be as successful as promised for at least 18% of their users.
The good news in this otherwise bleak post is that Verato has built a solution to this challenge. It’s a simple plug-in called Verato Auto-Steward™, and it leverages a powerful new approach to patient matching called “Referential Matching” to automatically find and resolve your EHR or EMPI technology's missed matches and duplicate records – without disrupting any of your existing processes or IT systems.
Referential Matching technology isn't simply a better algorithm – it is a totally new approach to patient matching. Rather than directly comparing the demographic data from two patient records to see if they match, Verato instead matches that demographic data to its comprehensive and continuously-updated reference database of identities. This proprietary database contains over 300 million identities spanning the entire U.S. population, and each identity contains a complete profile of demographic data spanning a 30-year history. It is essentially a pre-built answer key for all patient demographic data. By matching records to this database instead of to each other, Verato can make matches that conventional patient matching technologies could never make – even patient records containing demographic data that is out-of-date, incomplete, incorrect, or different.
Healthcare organizations and the vendors that are promising to aide those organizations should all address their internal patient matching problems, and the quickest and easiest way would be to plug Verato Auto-Steward into their EHR or EMPI to automatically find and resolve their missed matches and duplicate records.
White Paper: Improving Patient Matching with a Simple Plug-in