1. Patient Matching

2. Process Reviewed secondary (e.g. white papers) and primary literature on patient matching Series of teleconferences to establish scope, develop a framework and populate the framework

3. Assumptions There are multiple use cases with different trade-offs for sensitivity and specificity – our Summer Camp focus on patient care use case Establishing acceptable false positive rates is a policy and perhaps local decision based on what is possible/available in a market Focused on guidance around the EHR

4. Finding/Caveats The data necessary to provide explicit guidance on which patient attributes to “require” or for which to improve quality Some patient attributes (e.g. e-mail or zip code) vary significantly over time

5. Principles Specificity more critical than sensitivity – false positive rate will be critical Don’t preclude new attributes from being added to matching process

6. Flow Chart Query Source* Query Responder* Query Message Core attributes Optional attributes Query Response Message Matched patients Match metadata Captures and ensures quality of data needed to create query message Characterizes population of patients being matched * Note that an EMR may serve as both or either a query source and a query responder

7. Matching Fields Core –Name (last, first, middle initial) –Birth date –Gender (administrative?) Menu (some required to successfully match) –Address including zip (current and past) –Social security number –Maiden name –Full middle name –Healthcare provider (individual or institutional) –Visit information –Allow other assigned identifiers to support evolution

8. Data Quality Rational –Garbage-in Garbage-out –Align efforts to improve data with the importance of the data for matching (optimize value) Quality “assurance” –Consistent method to identify missing/unavailable data, approximate values or questionable values –Apply edits on a field by field basis Valid dates No future dates No SS# sub-fields with all 0s or all 9s Check that zip code is consistent with street address –Apply edits on a cross-field basis Check whether first name and gender are concordant

9. Data Formats / Content Build from IHE PDQ and XCDP(?) implementation guides –Expand on guidance for name structures especially Hispanic and Asian names –Add option to provide dates along with time variant data (e.g. zip codes, telephone numbers)

10. Match Quality Reporting What is reasonable to return with the matched patients? –Confidence level –Commonly occurring identifier flag

11. References Perspectives on Patient Matching: Approaches, Findings, and Challenges http://www.himss.org/content/files/PrivacySecurity/PIIWh itePaper.pdfhttp://www.himss.org/content/files/PrivacySecurity/PIIWh itePaper.pdf http://www.ihe.net/Technical_Framework/upload/IHE_ITI _Patient_Demo_Query_2004_08-15.pdf http://www.ihe.net/Technical_Framework/upload/IHE_ITI _TF_Supplement_XCPD_PC_2009-08-10.pdf