Applied clinical informatics · 2020
Igniting Harmonized Digital Clinical Quality Measurement through Terminology, CQL, and FHIR.
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McClure Robert C, Macumber Caroline L, Skapik Julia L, Smith Anne Marie
Аннотация
BACKGROUND: Electronic clinical quality measures (eCQMs) seek to quantify the adherence of health care to evidence-based standards. This requires a high level of consistency to reduce the effort of data collection and ensure comparisons are valid. Yet, there is considerable variability in local data capture, in the use of data standards and in implemented documentation processes, so organizations struggle to implement quality measures and extract data reliably for comparison across patients, providers, and systems.
OBJECTIVE: In this paper, we discuss opportunities for harmonization within and across eCQMs; specifically, at the level of the measure concept, the logical clauses or phrases, the data elements, and the codes and value sets.
METHODS: The authors, experts in measure development, quality assurance, standards and implementation, reviewed measure structure and content to describe the state of the art for measure analysis and harmonization. Our review resulted in the identification of four measure component levels for harmonization. We provide examples for harmonization of each of the four measure components based on experience with current quality measurement programs including the Centers for Medicare and Medicaid Services eCQM programs.
RESULTS: In general, there are significant issues with lack of harmonization across measure concepts, logical phrases, and data elements. This magnifies implementation problems, confuses users, and requires more elaborate data mapping and maintenance.
CONCLUSION: Comparisons using semantically equivalent data are needed to accurately measure performance and reduce workflow interruptions with the aim of reducing evidence-based care gaps. It comes as no surprise that electronic health record designed for purposes other than quality improvement and used within a fragmented care delivery system would benefit greatly from common data representation, measure harmony, and consistency. We suggest that by enabling measure authors and implementers to deliver consistent electronic quality measure content in four key areas; the industry can improve quality measurement.
OBJECTIVE: In this paper, we discuss opportunities for harmonization within and across eCQMs; specifically, at the level of the measure concept, the logical clauses or phrases, the data elements, and the codes and value sets.
METHODS: The authors, experts in measure development, quality assurance, standards and implementation, reviewed measure structure and content to describe the state of the art for measure analysis and harmonization. Our review resulted in the identification of four measure component levels for harmonization. We provide examples for harmonization of each of the four measure components based on experience with current quality measurement programs including the Centers for Medicare and Medicaid Services eCQM programs.
RESULTS: In general, there are significant issues with lack of harmonization across measure concepts, logical phrases, and data elements. This magnifies implementation problems, confuses users, and requires more elaborate data mapping and maintenance.
CONCLUSION: Comparisons using semantically equivalent data are needed to accurately measure performance and reduce workflow interruptions with the aim of reducing evidence-based care gaps. It comes as no surprise that electronic health record designed for purposes other than quality improvement and used within a fragmented care delivery system would benefit greatly from common data representation, measure harmony, and consistency. We suggest that by enabling measure authors and implementers to deliver consistent electronic quality measure content in four key areas; the industry can improve quality measurement.