The Office of the National Coordinator for Health Information Technology has released initial findings from a study to assess current industry capabilities for matching patients with their health information.
The Patient Identification and Matching stakeholder initiative included an environmental scan comprising interviews with more than 50 large health systems and software vendors (electronic health records, master data management/master patient index and health information exchange).
The complete report was available to participants in a Web seminar on December 16. ONC is not publicly posting it because the findings are not policies, but ideas that merit more consideration. They include:
Mandate standardized patient identifying attributes in relevant exchange transactions using Health Level Seven, Consolidated Clinical Document Architecture, Integrating the Healthcare Enterprise and eHealth Exchange standards. These attributes include first/given name, last/family name, middle/second given name, suffix, date of birth in a standard format, current and historical addresses, phone numbers and gender. The attributes are generally highly variable from an implementation standpoint, with few fields being required and little to no standardization of the data attributes themselves, according to the findings.
Certification criteria should be introduced that require certified EHR technology to capture the data attributes that would be required in the standardized patient identifying attributes. The majority of the data attributes listed above are currently captured by EHR systems. However, at least one attribute, historical address, is not consistently captured across all vendors. In addition, while all vendors capture first and last name, some do not have the capability to capture hyphens or apostrophes in the name fields. This leads to inconsistencies when sharing first and last name that may cause false negatives for systems utilizing deterministic matching.
Study the ability of additional, non-traditional data attributes to improve patient matching. "These data attributes could include email address, mother's first and maiden name, father's first and last name, place of birth, driver's license number, passport number, or eye color. Currently, EHR systems cannot capture the majority of these data attributes in a structured field."
Develop or support an open source algorithm that could be utilized by vendors to test the accuracy of their patient matching algorithms or be utilized by vendors that do not currently have patient matching capabilities built into their systems. During the environmental scan, many indicated that replacing their current systems would be cost prohibitive. As such, it is not suggested that a standardized patient matching algorithm be developed or required. In a more limited way, however, there is value in developing an open source algorithm or updating and supporting an existing open source algorithm that EHR vendors may choose to utilize in their products.
Certification criteria should be introduced that requires certified EHR technology that performs patient matching to demonstrate the ability to generate and provide to end users reports that detail potential duplicate patient records. Further, certified EHR technology should clearly define for users the process for correcting duplicate records, which typically requires the merging of records.
Build on the initial best practices that emerged during the environmental scan by convening industry stakeholders to consider a more formal structure for establishing best practices for the matching process and data governance. The practices include regular reviews of potential duplicates, data governance programs that work to establish current rates and then improve false positive and false negative rates, training programs that can be replicated, policies that apply across a health system with multiple sites, and processes for a central entity--such as a health information organization or accountable care organization--to notify participants of matching errors and corrections.
Develop best practices and policies to encourage consumers to keep their information current and accurate. Meaningful use Stage 2 places an increased emphasis on patient engagement with their health information. This emphasis should be extended to ensuring patients are engaged in maintaining accurate demographic data.
Work with stakeholders to develop and disseminate educational and training materials on best practices for verifying patient data attributes. Accurate patient identification and matching across organizations cannot be adequately addressed through standardization of data attributes alone. The accuracy of the data attributes themselves is important in minimizing false positives and false negatives. While some systems are equipped with algorithms that can compensate for data accuracy issues using probabilistic matching techniques, these systems have limitations.
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