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Igniting Harmonized Digital Clinical Quality Measurement through Terminology, CQL, and FHIR
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Georg Thieme Verlag KG
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6949169/ https://www.ncbi.nlm.nih.gov/pubmed/31914472 http://dx.doi.org/10.1055/s-0039-3402755 |
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author | McClure, Robert C. Macumber, Caroline L. Skapik, Julia L. Smith, Anne Marie |
author_facet | McClure, Robert C. Macumber, Caroline L. Skapik, Julia L. Smith, Anne Marie |
author_sort | McClure, Robert C. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6949169 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Georg Thieme Verlag KG |
record_format | MEDLINE/PubMed |
spelling | pubmed-69491692021-01-01 Igniting Harmonized Digital Clinical Quality Measurement through Terminology, CQL, and FHIR McClure, Robert C. Macumber, Caroline L. Skapik, Julia L. Smith, Anne Marie Appl Clin Inform 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. Georg Thieme Verlag KG 2020-01 2020-01-08 /pmc/articles/PMC6949169/ /pubmed/31914472 http://dx.doi.org/10.1055/s-0039-3402755 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited. |
spellingShingle | McClure, Robert C. Macumber, Caroline L. Skapik, Julia L. Smith, Anne Marie Igniting Harmonized Digital Clinical Quality Measurement through Terminology, CQL, and FHIR |
title | Igniting Harmonized Digital Clinical Quality Measurement through Terminology, CQL, and FHIR |
title_full | Igniting Harmonized Digital Clinical Quality Measurement through Terminology, CQL, and FHIR |
title_fullStr | Igniting Harmonized Digital Clinical Quality Measurement through Terminology, CQL, and FHIR |
title_full_unstemmed | Igniting Harmonized Digital Clinical Quality Measurement through Terminology, CQL, and FHIR |
title_short | Igniting Harmonized Digital Clinical Quality Measurement through Terminology, CQL, and FHIR |
title_sort | igniting harmonized digital clinical quality measurement through terminology, cql, and fhir |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6949169/ https://www.ncbi.nlm.nih.gov/pubmed/31914472 http://dx.doi.org/10.1055/s-0039-3402755 |
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