Poor patient matching is holding healthcare back – the time has come to work together to fix it
Inaccurate patient identification is not only a major impediment to interoperability and patient safety, but a massive burden financially—costing the U.S. healthcare system over $6 billion annually.
The good news is that for the first time, federal officials are seeking guidance from experts in the public and private sector on how to improve patient matching in the form of two separate request for information (RFIs) as part of the proposed rules to promote interoperability.
After having invested more than two decades addressing this issue, we at NextGate saw this as a unique opportunity to draw on our experiences from the past twenty years and classify the components of a successful framework to strengthen nationwide record matching accuracy. You can find our detailed response to the Centers for Medicare & Medicaid Services (CMS) and the Office of the National Coordinator of Health IT (ONC) RFIs here.
As a company dedicated to eradicating duplicates and helping organizations obtain a clear and all-inclusive view of an individual’s care history, we know an algorithm-based enterprise master patient index (EMPI), augmented with other technologies, and combined with policies to improve data quality at the point of capture, is the most promising strategy in patient matching effectiveness.
Imposing a national patient identifier is not the answer to solving this issue—which is fundamentally the result of proprietary systems unwilling to integrate or communicate. Despite the fact that both England and Scotland have a mandated number, both still require an EMPI platform to provide duplicate detection services, enforce data capture workflows, and allow systems to easily connect to a demographic lookup service using the latest messaging standards. In addition, the EMPI maintains relationship data so that the association between a patient and a general practitioner can be centrally managed. In fact, even with the NHS number in place, systems in England continue to struggle with incorrect NHS numbers and multiple NHS identifiers being applied to one patient.
Since the U.S. healthcare system is much larger in scope and far more complex than that of the U.K. and Scotland—we should expect the difficulties in implementing a unique patient identifier to be proportionally greater. It would be much more useful to build on the activities of healthcare organizations and communities (e.g., multiple hospitals pointing to the same EMPI) to implement EMPI-based strategies that integrate disparate systems and that emphasize the need for continual increases in the quality of the underlying data used for matching.
Relying solely on EHRs is risky
Standardizing the data in EHRs, as recommended in a recent report from the Pew Charitable Trusts, is important in improving the quality of the data being matched. However, the industry should not bank on EHRs to solve the patient matching problem because no matter how well data is standardized, EHR-level requirements can only go so far.
As outlined in our RFI responses, keeping identifiers and demographics in localized silos of data is an undesirable model for healthcare. The core identity of a patient should not be in the control of any single system, rather this information must be externalized from such insulated applications to maintain accuracy and consistency across all connected systems within the delivery network.
Private/Public collaboration is critical
Several previous reports have identified specific examples of the private and public sectors working cooperatively to tackle patient matching. To truly move the needle forward requires the right mix of technology, process and stakeholder collaboration.
Accurate patient identification is crucial if we are to achieve true interoperability of patient records and we need to work together to make it happen.