We are on the precipice of a patient matching revolution.

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. This is because the incremental improvements in patient matching technologies have been vastly outpaced by the exponentially growing challenges those technologies are facing—like new data sources, deteriorating data quality, health information exchange, and mergers and acquisitions.

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.

2019 will mark the beginning of a patient matching revolution in which health systems turn to innovative new technologies to solve their patient matching challenges and to eliminate duplicate medical records once and for all.

Yet the revolution may already be afoot.

In early October, The Pew Charitable Trusts released a landmark report highlighting four cornerstone opportunities to improve patient matching nationwide—biometrics, smartphone-based solutions, demographic data standardization, and referential matching. While data standardization has been attempted for decades without consistent success, and while biometric and smartphone-based strategies are still nebulous, referential matching is an innovative, powerful, and widely-used solution that represents a quantum-leap improvement in patient matching technology. It achieves unprecedented levels of accuracy, scalability, cost-effectiveness, and ease of deployment and operation.

Referential matching will spearhead the patient matching revolution in 2019, but two things will drive the revolution and spur health systems into action: artificial intelligence and ethics.

Artificial Intelligence

Artificial intelligence (AI) will be singular in both the depth and breadth of its impact across healthcare delivery and operations—from helping physicians make more accurate and consistent diagnoses, to analyzing lab and imaging results, to taking and inputting clinical notes, to automating hospital operations, and to facilitating interoperability and health information exchange.

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.

Ultimately, automating the discovery and resolution of duplicate records with referential matching technology can reduce claims denials to save up to $1.5 million, reduce operational costs by at least $200,000, improve the ROI of EHR deployments, and enable value-based care and patient engagement initiatives by enabling more complete and accurate patient health histories.

Ethics

Health systems are increasingly making technology investments not just to reduce costs or improve efficiencies, but also because not using new technologies is becoming unethical. We have reached a tipping point where innovative new technologies are prominent, successful, and inexpensive enough for ethics to begin driving technology purchasing decisions.

For example, if AI can make more accurate and consistent diagnoses than a doctor in less time, then it is unethical not to begin using AI to assist in making these diagnoses. And if telehealth can mean caring for more patients while reducing the patient’s burdens to seek and receive care, then it is unethical not to begin to use telehealth services.

In the same vein, if referential matching technology can help doctors to see a more complete patient health history at the point of care, it is unethical for health systems not to use referential matching technology. This is especially true when considering that referential matching technology is a quantum leap, more accurate than any EHR or EMPI, at resolving patient identities and connecting patient data. It can easily plug into any EHR or EMPI to help it match more records, find more duplicates, and assemble more complete longitudinal health records.

Over 85 percent of providers have witnessed or know of a medical error that is the result of patient misidentification or inaccurate patient matching. Needless to say, this is unacceptable—and easily preventable with referential matching technology.

Photo: OnstOn, Getty Images