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Batched lab-result data is routinely sent by laboratories to medical groups and health plans to assist in their calculation of pay-for-performance (P4P) measures. However, certain technical and workflow problems related to patient-identity matching cause this lab data to be incomplete or unusable for P4P calculations. Given the importance of accurate P4P reporting, The California Association of Physician Groups (CAPG) and the California HealthCare Foundation (CHCF) commissioned Sujansky & Associates to investigate these patient-matching problems and to suggest measures to mitigate or eliminate them.
Issues in the Delivery and Integration of Retrospective Lab Data: Analysis and Recommendations (PDF)
Patient Data Matching Software: A Buyer's Guide for the Budget Conscious (PDF)
- Interviews with key stakeholders from hospital and reference laboratories, provider organizations, data integrators, and health plans
- Qualitative analysis of the workflow processes involved in ordering, performing, reporting, and integrating lab tests, as well as the variations in these processes that exist among
business entities
- Quantitative analysis of actual lab data-matching techniques and lab data-matching results to estimate the magnitude of the problems and to statistically analyze their various causes
- Formulation of a set of near-term, medium-term, and long-term recommendations to improve the use of batched lab data, based on our findings
- Documentation of analysis and recommendations in a comprehensive report (see below)
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The proportion of lab data excluded from P4P calculations may be as low as 2% or as high as 20%, depending on the laboratory and medical group involved.
Low-cost probabilistic matching software can improve the rate of lab data matching by as much as 17 percentage points.
The rate at which patient lab result data is successfully integrated into P4P databases varies significantly based on the individual physician who orders the test.
Changes in the management of health plan member ID cards could significantly improve the completeness of lab result data in P4P databases.
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