One of the biggest new product announcement splashes of this year so far 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patient matching approaches rely on demographic data that often changes, leaving records full of incorrect or out-of-date information.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 there are approaches beyond demographic data that can improve the industry’s patient matching ability. One such approach is called “Referential Matching,” which compares records to a reference database rather than just directly to each other. The database becomes an “answer key” for demographic data, making matches that conventional patient matching technologies can never make. It can work well with other approaches such as biometrics that provide positive identification at point of care.
Healthcare organizations and the vendors that are promising to aide those organizations should all address their internal patient matching problems. Once patient matching is solved, other groundbreaking interoperability technology will finally shine.