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HIT Policy Committee Privacy and Security Tiger Team

HIT Policy Committee Privacy and Security Tiger Team. Deven McGraw, Chair Paul Egerman, Co-Chair Patient Matching Recommendations February 2, 2011. Tiger Team Members. Deven McGraw, Chair , Center for Democracy & Technology Paul Egerman, Co-Chair Dixie Baker , SAIC

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HIT Policy Committee Privacy and Security Tiger Team

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  1. HIT Policy CommitteePrivacy and Security Tiger Team Deven McGraw, Chair Paul Egerman, Co-Chair Patient Matching Recommendations February 2, 2011

  2. Tiger Team Members Deven McGraw, Chair, Center for Democracy & Technology Paul Egerman, Co-Chair Dixie Baker, SAIC Christine Bechtel, National Partnership for Women & Families Rachel Block, NYS Department of Health Neil Calman, Institute for Family Health Carol Diamond, Markle Foundation Judy Faulkner, EPIC Systems Corp. Leslie Francis, University of Utah; NCVHS Gayle Harrell, Consumer Representative/Florida John Houston, University of Pittsburgh Medical Center David Lansky, Pacific Business Group on Health David McCallie, Cerner Corp. Wes Rishel, Gartner Latanya Sweeney, Carnegie Mellon University Micky Tripathi, Massachusetts eHealth Collaborative Adam Greene, Office of Civil Rights Joy Pritts, ONC Judy Sparrow, ONC

  3. Scope of this Discussion • Accurately matching individuals or patients to their health information has a number of benefits: • improved patient outcomes • improved patient safety • greater efficiency • improved fraud detection • promoting data integrity • reduced inappropriate data exposure

  4. Patient Matching Challenges • It is not possible to achieve perfection in patient matching • Inaccuracy is not just a technology problem – there is a significant human component • Poor data quality (both accuracy and completeness) significantly inhibits the ability to accurately match patients • There is little research on best practices • There is no one-size-fits-all solution • Challenges increase as data gets further removed from the source, and when more sources of data are involved • Universal identifiers for patients/individuals could be helpful but are not a panacea

  5. Recommendation 1: Standardized Formats for Demographic Data Fields The use of any particular data field should not be required for matching. However, when a data field is used to match, a standardized format will help increase accuracy. Therefore, the Standards Committee should: • Propose standard formats for data fields that are commonly used in matching patients to their data (for example: name, DOB, zip, address, and gender) • EHRs should be tested and certified for interoperability re: standard data fields – from data entry through data transfer • Develop recommendations on situations when information is not available to complete a data field & how systems should represent missing data when it is exchanged (e.g. CCD/CCR) • Consider whether a USPS validation/normalization program would be beneficial

  6. Recommendation 2: Internally Evaluating Matching Accuracy • Health care organizations/entities should evaluate the effectiveness of their matching strategies in achieving matching accuracy on an ongoing basis • This should include individual providers, institutions and HIEs • Organizations/entities should use such evaluations to internally improve matching accuracy

  7. Recommendation 3: Accountability • Matching accuracy should be enforced through NHIN/HIE governance of participating organizations • HIEs should be required to establish programs to ensure that acceptable levels of matching accuracy are maintained by its participants. Such programs should: • Be appropriate for the populations served by the HIE, and the purposes for which data can be accessed in or via the HIE • Include tracking of who has accessed a patient’s information • Require participants that do not meet standards to undertake remedial action • Be consistent with industry best practices • HIEs also should have policies on how participants must respond when information is incorrectly matched

  8. Recommendation 4: Developing, Promoting and Disseminating Best Practices • ONC should establish a program or programs to develop and disseminate best practices in improving data capture and matching accuracy. For example, such program or programs should: • Gather and disseminate evidence about “what works” • Establish programs for transparency re: the efficacy of matching algorithms • Pilot and test accuracy of matching strategies • Where funds are available, fund further development of innovative matching strategies • Develop and promulgate best practices for propagating record corrections

  9. Recommendation 5: Supporting the role of the individual/patient • The Tiger Team supports the efforts of the Meaningful Use Workgroup and the Policy Committee to increase the access of individuals to their health information and improve its accuracy • The Tiger Team supports establishment of audit trails that facilitate patients seeing where their information has been accessed through exchanges such as HIEs • Individuals and patients should have an easy to understand and access process for reporting corrections to their information • If an HIE is required via business associate or participation agreement to patients with access to their health information, provide access to patients, they should be required to have policies to support such access to data and requests for corrections

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