CC BY-NC-ND 4.0 · Appl Clin Inform 2020; 11(01): 023-033
DOI: 10.1055/s-0039-3402755
AMIA CIC 2019
Georg Thieme Verlag KG Stuttgart · New York

Igniting Harmonized Digital Clinical Quality Measurement through Terminology, CQL, and FHIR

Robert C. McClure
1  MD Partners, Inc., Lafayette, Colorado, United States
,
Caroline L. Macumber
2  Apelon, Inc., Hartford, Connecticut, United States
,
Julia L. Skapik
3  Division of Clinical Affairs, National Association of Community Health Centers, Inc., Bethesda, Maryland, United States
,
Anne Marie Smith
4  Department of Measure Validation, National Committee for Quality Assurance, Washington, District of Columbia, United States
› Author Affiliations
Further Information

Publication History

05 September 2019

02 December 2019

Publication Date:
08 January 2020 (online)

  

Abstract

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.