Duplicate Patient Records as a Major and Costly EHR and EMPI Problem

I have posted a number of notes about the use of IT to improve efficiency and effectiveness in healthcare. A gnarly but persistent problem has been duplicate patent records and substantial IT resources are required to correct the problem. Below is an excerpt from an article on this topic (see: Artificial Intelligence and ethics will drive a patient matching revolution in 2019):

Existing patient matching technologies are failing, as evidenced by skyrocketing rates of duplicate records in electronic health record (EHR) and enterprise master patient index (EMPI) systems. In fact, duplicate record rates have nearly doubled in the past decade, from an average of 10 percent in 2008 to 18 percent today....These patient matching technologies are failing at the worst possible time—as more and more of a health system’s strategic initiatives fundamentally rely on accurate and complete patient data. The dire consequences of these failures include a third of claims being denied costing $1.5 million annually; massive operational inefficiencies costing $200,000 annually; lowered return on investment (ROI) of EHR deployments; inhibited value-based care initiatives; and drastic consequences to patient safety, care quality, and patient satisfaction....

Yet nowhere can AI have a more immediate and accessible impact than in patient matching. Currently, health systems have teams of data stewards and health information management (HIM) professionals dedicated to finding, reviewing, researching, and resolving records that their EHR or EMPI has flagged as “potential duplicates.” Essentially, these employees are spending hours each day looking at, for example, a record for Jane Jones and another for Jane Smith, trying to decide if both Janes are actually the same person and if her records should be merged. Referential matching technology can automate 50-to-75 percent of this manual effort by being an intelligent and data-driven technology. It can automatically find and resolve duplicate records that EHRs and EMPIs have missed, enabling data stewards and HIM staff to focus on higher-value projects—while simultaneously lowering the operational costs and inefficiencies plaguing health systems by automating manual work.

Here's the way that a company called Verato has tried to solve the patient matching problem (see: Referential Matching: The Silver Bullet for Patient Identity and Patient Matching):

Verato has pioneered a powerful new patient matching technology called “Referential 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.

I am sure that there will be many new solutions to this patient matching problem utilizing AI and ever more sophisticated algorithms that do not require a large, remote, proprietary patient database. Nevertheless, attention is now being paid to this major medical record problem that endangers patient lives -- that's the right direction. I will continue to monitor this field to see what other solutions emerge.