ONC offers recommendations to begin improving patient data matching

The Office of the National Coordinator for Health IT (ONC) released initial findings from a patient identification and matching study during a Dec. 16 meeting on the subject.

ONC announced in September it would launch a collaborative project to drive better matching between patients and their data during health information exchange.

The study included interviews with more than 50 large health systems, EHR vendors and other stakeholders, said Lee Stevens, policy director of ONC's state health information exchange program.

“Care coordination is one of the most important things we think about,” said Judy Murphy, RN, ONC's deputy national coordinator for programs and policy, and was significant in Meaningful Use (MU) Stage 2 and will be a significant focus in Stage 3.

The best way to proceed with patient matching, Stevens said, is determining what’s working today. To that end, ONC launched a literature review and environmental scan. “We talked from the beginning about the imperative that we want something implementable in the near term, a first step toward meaningful improvement in patient matching.”  

Improvements will be multifaceted and incremental with no single solution or step that is final, he said. “Potential improvements should apply to all sizes and types of provider settings. Patient safety is the overarching principle.”

The initial findings uncovered several obstacles preventing accurate patient matching that served as the basis for the following recommendations:

• Require standardized patient identifying attributes in the relevant exchange transactions. “A lack of data attributes that are populated consistently and in a standardized format has been identified by the industry as a major impediment to more accurate patient matching,” said Scott Afzal, principal of Audacious Inquiry, the consulting firm charged with researching the issue.
• Introduce certification criteria that require certified EHR technology (CEHRT) to capture the data attributes that would be required in the standardized patient identifying attributes.
• Study the ability of additional, nontraditional data attributes to improve patient matching. Statistical analysis can be performed with these data attributes to assess their ability to improve patient matching, Afzal said.
• Develop or support an open source algorithm that could be used by vendors to test the accuracy of their patient matching algorithms or be used by vendors that do not currently have patient matching capabilities built into their systems. “We found a huge range of capabilities out there,” he said. “Smaller practices and hospitals don’t have a focus on identity management, however, nor the tools to help them do well with it.”
• Introduce certification that requires CEHRT that performs patient matching to demonstrate the ability to detect potential duplicate patient records. CEHRT should clearly define for users the process for correcting duplicate records which typically requires the merging of records, Afzal said. “Identifying duplicate records is important to ensuring accurate matching. Not all EHR systems currently provide these reports to their users. You can’t measure what isn’t defined.”
• Build on the initial best practices from the environmental scan by developing a more formal structure for establishing best practices for the matching process and data governance. “As we talked to healthcare systems, we found that those doing the best put a focus on it, developing programs internally.”
• Develop policies to encourage consumers to keep their health information accurate and up-to-date. “Patient engagement efforts have not yet evolved to ensure that consumers can routinely access their demographic information to inform and update it, either with the help of staff or independently via patient portal,” said Afzal.