Description
The panel will discuss how existing efforts to expresselectronic clinical quality measures (eCQMs) have been mapped to new, evolving standards to enable and encourage continuity with emerging health information technology (IT) efforts without disrupting existing efforts to evaluate clinical care in current healthcare settings. The work is based on efforts to harmonize standards for quality measurement and clinical decision support (CDS) using the Health Level Seven International (HL7) logical representation, Quality Improvement Clinical Knowledge (QUICK)data model, and HL7 Fast Healthcare Interoperability Resources (FHIR) Clinical Reasoning resources as the structural components and HL7 Clinical Quality Language (CQL) as the expression language. By the end of the presentations the attendees will:
●Understand the issues in defining data models and traversing concepts between data models.
●Learn challenges aligning requirements for retrospective performance measurement with existing methods by which data are captured during routine care delivery using clinical software.
●Learn the process for translating measurement concepts in published eCQMs to newly emerging CDS standards to provide consistent information retrieval, improve workflow, reduce burden, and increase performance rates.
●Describe current challenges with mapping existing eCQM data elements to existing EHR data elements to accurately capture the quality of care being delivered.
Learning Objective: Understand the issues in defining data models and traversing concepts between data models.
Learn challenges aligning requirements for retrospective performance measurement with existing methods by which data are captured during routine care delivery using clinical software.
Learn the process for translating measurement concepts in published eCQMs to newly emerging CDS standards to provide consistent information retrieval, improve workflow, reduce burden, and increase performance rates.
Describe current challenges with mapping existing eCQM data elements to existing EHR data elements to accurately capture the quality of care being delivered.
Authors:
Floyd Eisenberg (Presenter)
ESAC, Inc.
Claude Nanjo (Presenter)
University of Utah
Juliet Rubini (Presenter)
Mathematica Policy Research
Joseph Kunisch (Presenter)
Memorial-Hermann Healthcare System
Kathy Lesh (Presenter)
Battelle Memorial Institute
Presentation Materials: