While working as a nurse, I remember one instance of caring for two patients who were twins. Along with the same birthday and the same last name, they both had very similar first names. The twins were roughly around the same height, the same weight, and also had the same hair color. At first glance of their medical record, they could almost be the same person.
When reviewing their medical history, I discovered surgical records from one had been incorrectly imported into the chart of the other. The chart for the first twin documented an appendectomy while the other twin (who had received the procedure) had no record of it. This wasn’t an emergency situation–the error was easily corrected. But the confusion that would have occurred in an emergency could have led to a life-threatening situation.
Patient matching works to ensure the correct patient information ends up in the correct medical record. It is no surprise that accurately identifying patients improves care, reduces the risk of errors, and reduces inefficiencies like unnecessary tests. However, patient matching errors frequently occur in our healthcare system. A recent report from the eHealth Initiative Foundation and NextGate confirmed that, in the past two years, 38% of U.S. healthcare providers [PDF] reported an adverse event due to a patient matching issue.
Errors in Patient Matching
First, a huge problem when it comes to patient matching is human error. A small typo can create inconsistencies, making it difficult to find or verify patient information. The eHealth report (linked above) identified data entry errors as the leading cause of duplicate medical records.
Second, there’s a lack of standard data collection across electronic health records (EHRs). For instance, at one doctor’s office my name is documented as “Alexa Ortiz”. At another doctor’s office (where my middle initial is collected) my name is “Alexa M. Ortiz”. Even one letter can have a large impact on the linkage of data across medical records.
Efforts to Address Issues
Further heightening the importance of patient matching is its role in achieving interoperability for EHRs. This is a long-held goal for both the public and private sector. With that in mind, there have been several initiatives to address ongoing issues.
The Office of the National Coordinator for Health Information Technology (ONC) funded a Patient Matching Algorithm Challenge in 2017. It aimed to improve patient matching algorithms and increase the measurement of how algorithms perform. And in 2019, the Centers for Medicare & Medicaid Services requested feedback on how to use its authority to improve the identification of patients and encourage better coordination of care.
Outside of federal agencies, The Sequoia Project and the Care Connectivity Consortium released a patient matching framework in 2015. It was updated in 2018 [PDF]. This framework included patient matching guidelines and a model for organizations to measure their progress. In early 2020, the Pew Charitable Trusts also announced the development of a roadmap to support use of biometrics-enhanced patient matching.
What About Unique Identifiers?
Another frequently discussed solution is the creation of a unique patient identifier. This offers a way to link patient records through an ID that would move with patients across healthcare facilities. There is discussion about lifting a pre-existing ban on the U.S. Department of Health and Human Services from using funds to develop such an identifier. This is a hot topic, but even with support from multiple organizations (such as the College of Healthcare Information Management Executives and others [PDF]) the ban has not been overturned.
If the ban were overturned, there are still cost-related and technical issues that remain. The cost of developing an identifier is estimated to be between $4.9 billion and $12.2 billion [PDF] (adjusted to 2009 dollars). From a technical standpoint, a back-up method would still be needed for when a universal identifier is not available (for instance, when reviewing historical records).
Patient Matching & COVID-19
Patient matching issues have rapidly moved into the forefront given the current public health crisis. With the coronavirus outbreak, it’s imperative the correct information is associated with the correct person. While there are numerous patient matching efforts to prevent errors, current solutions have yet to offer a panacea. ONC’s newly released Cures Act Final Rule aims to standardize certain data elements and application programming interfaces, which has the potential to improve patient matching.
More research is needed around strategies to ensure the right data is routinely linked with the right person (no matter how similar they appear at first glance). Addressing patient matching promises improved interoperability, quality of care, and patient safety.
Alexa Ortiz