Saturday, November 3, 2012

Pre-Symposium Tutorial:  8:30 AM – 4:30 PM

Presenters: Bob Dolin, Floyd Eisenberg and Gaye Dolin 

This tutorial will introduce the audience to the logistic, technical and semantic aspects of expressing, processing and reporting from Electronic Health Records (EHRs). We begin with an overview of the "end-to-end" quality measurement process and define the infrastructure requirements. The tutorial will clearly explain how efforts by the National Quality Forum (NQF), and Health Level Seven (HL7) are converging to collaboratively meet the needs of the Office of the National Coordinator’s (ONC) EHR certification regulations and the Centers for Medicaid and Medicare Services (CMS) "Meaningful Use" (MU) requirements NQF has been instrumental here, in part through their rigorous clinical quality measure endorsement process, and in part through their development of the Quality Data Model (QDM), which is a domain analysis model of the types of criteria recurrently found in quality measures (such as "active diagnosis", "discharge medication"). The QDM forms a foundational basis for the semantic interoperability of quality measures. HL7’s role in the collaboration is to turn quality requirements and the QDM into formal computer processable specifications, that work together seamlessly to support the quality reporting process. As part of that process, HL7 has developed the Health Quality Measure Format (HQMF) standard (also referred to as the eMeasure standard) for formalizing the logic in clinical quality measures; and the Quality Reporting Document Architecture (QRDA) standard for communicating patient-level quality data. HQMF and QRDA work with other MU standards, such that a MU-certified EHR can (in theory) automatically process an eMeasure – import it, turn it in to dynamic queries to extract relevant patient data, package up the data, and transmit it securely to CMS or some other quality reporting agency in QRDA format. As we will describe, the process for EHR-based quality reporting is being studied for its applicability to distributed decision support and distributed population-based public health reporting.

www.amia.org/amia2012