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

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.

Keywords

knowledge modeling and representation - clinical practice guideline - quality indicators, health care - vocabulary, controlled - HL7 CQL

• References

• 1 Available at: https://www.healthsystemtracker.org/chart-collection/quality-u-s-healthcare-system-compare-countries/#item-start . Accessed July 15, 2019
• 2 Available at: https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/Value-Based-Programs.html . Accessed October 26, 2019
• 3 Ryan AM, Rodgers PE. Linking quality and spending to measure value for people with serious illness. J Palliat Med 2018; 21 (S2): S74-S80
• 4 Saver BG, Martin SA, Adler RN. , et al. Care that matters: quality measurement and health care. PLoS Med 2015; 12 (11) e1001902
• 5 Available at: https://ecqi.healthit.gov/content/ecqms . Accessed July 10, 2019
• 6 D'Amore JD, Li C, McCrary L. , et al. Using clinical data standards to measure quality: a new approach. Appl Clin Inform 2018; 9 (02) 422-431
• 7 Johnson SG, Speedie S, Simon G, Kumar V, Westra BL. Quantifying the effect of data quality on the validity of an eMeasure. Appl Clin Inform 2017; 8 (04) 1012-1021
• 8 Available at: https://ecqi.healthit.gov/qdm-quality-data-model . Accessed July 21, 2019
• 9 The current balloted version of CQL is R1, STU4, Available at: https://cql.hl7.org/2019May/index.html
• 10 Available at: http://hl7.org/implement/standards/fhir/ . Accessed July 20, 2019
• 11 Available at: http://build.fhir.org/ig/cqframework/qi-core/ . Accessed July 22, 2019
• 12 Commercial references to use of CQL include Apervita: Available at: https://apervita.com/cql/ , Ablehealth: https://ablehealth.com/2017/11/17/a-new-era-for-measure-development-clinical-quality-language-is-here/
• 13 Available at: http://store.ncqa.org/index.php/performance-measurement/hedis-volume-2-digital-measure-bundles-comparison.html?___SID=U
• 14 See the following: CDC Adapting Clinical Guidelines for the Digital Age. Available at: https://www.cdc.gov/ddphss/clinical-guidelines/index.html . Accessed October 31, 2019. FHIR Clinical Guidelines implementation guide current build. Available at: http://build.fhir.org/ig/HL7/cqf-recommendations/ . Accessed October 31, 2019
• 15 National Quality Forum (NQF). Guidance for measure harmonization: a consensus report, Washington, DC: NQF. Available at: https://www.qualityforum.org/Publications/2011/05/Guidance_for_Measure_Harmonization.aspx . 2010
• 16 The eCQM measure is no longer active. The most recent description available can be found here. Available at: https://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/Downloads/eCQM_2016EPEC_MeasuresTable.pdf . For current information on the HRSA HIV Medical Visit Frequency measure. Available at: https://hab.hrsa.gov/sites/default/files/hab/clinical-quality-management/coremeasures.pdf
• 17 Available at: https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityMeasures/CMS-Measures-Inventory.html . Accessed October 26, 2019
• 18 NQF QDM. Available at: http://www.qualityforum.org/QualityDataModel.aspx . Accessed July 10, 2019
• 19 Thompson WK, Rasmussen LV, Pacheco JA. , et al. An evaluation of the NQF quality data model for representing electronic health record driven phenotyping algorithms. AMIA Annu Symp Proc 2012; 2012: 911-920
• 20 “A QDM data element encapsulates a certain category with an associated datatype.”. Available at: https://ecqi.healthit.gov/glossary/qdm-data-element . Accessed October 31, 2019
• 21 QDM uses the phrase “datatype” differently than the traditional programing “data type.” QDM datatypes are a combination of data Category and context of use where the implementer must understand the implications of the context of use. For example, ‘Medication, Active’ is different than ‘Medication, Administered’ because an administration is a documented event and represents a “different part of the clinical care process.”
• 22 Available at: https://ecqi.healthit.gov . Accessed July 21, 2019
• 23 HL7 Specification: Characteristics of a Value Set Definition, Release 1. Available at: http://www.hl7.org/implement/standards/product_brief.cfm?product_id=437
• 24 National Quality Forum (NQF). Value Set Harmonization: Final Report, Washington, DC: NQF. Available at: https://www.qualityforum.org/Publications/2016/03/Value_Set_Harmonization_-_Final_Report.aspx . 2016
• 25 Available at: https://vsac.nlm.nih.gov/ . Accessed October 25, 2012
• 26 Bodenreider O, Nguyen D, Chiang P. , et al. The NLM value set authority center. Stud Health Technol Inform 2013; 192: 1224
• 27 Available at: https://cmit.cms.gov/CMIT_public/ListMeasures . Accessed July 10, 2019
• 28 “Concept Domain” is defined in HL7 as a named category or class of concepts. For a formal definition of Concept Domains, see HL7s description as part of the V3 messaging standard. Available at: https://wiki.hl7.org/Requirements-Concept_Domains . Accessed October 31, 2019
• 29 HL7® FHIR® Product brief, Available at: http://www.hl7.org/implement/standards/product_brief.cfm?product_id=491 . Directory of versions, Available at: http://www.hl7.org/fhir/directory.cfml
• 30 Matney SA, Heale B, Hasley S. , et al. Lessons learned in creating interoperable fast healthcare interoperability resources profiles for large-scale public health programs. Appl Clin Inform 2019; 10 (01) 87-95
• 31 CQL - Clinical Quality Language. Available at: https://ecqi.healthit.gov/cql-clinical-quality-language . Accessed July 21, 2019
• 32 HL7 cross-paradigm specification: clinical quality language, release 1. Available at: http://www.hl7.org/implement/standards/product_brief.cfm?product_id=400 . Accessed July 21, 2019
• 33 U.S. Core Data for Interoperability (USCDI) Accessed. Available at: https://www.healthit.gov/isa/us-core-data-interoperability-uscdi
• 34 Quality Improvement Core (QI-Core) Implementation Guide. Available at: http://hl7.org/fhir/us/qicore/index.html . Accessed August 28, 2019
• 35 Available at: https://www.healthit.gov/buzz-blog/interoperability/heat-wave-the-u-s-is-poised-to-catch-fhir-in-2019
• 36 Available at: https://www.healthcareitnews.com/news/cerner-touts-adoption-normative-fhir-r4-standard
• 37 Middleton B, Platt JE, Richardson JE, Blumenfeld BH. recommendations for building and maintaining trust in clinical decision support knowledge artifacts. Research Triangle Park. North Carolina: Patient-Centered Clinical Decision Support Learning Network. Available at: https://pccds-ln.org/sites/default/files/2018-09/TFWG%20White%20Paper_final.pdf . Accessed 2018
• 38 Devine EB, Van Eaton E, Zadworny ME. , et al. Automating electronic clinical data capture for quality improvement and research: the CERTAIN validation project of real world evidence. EGEMS (Wash DC) 2018; 6 (01) 8
• 39 Zhu VJ, Walker TD, Warren RW, Jenny PB, Meystre S, Lenert LA. Identifying falls risk screenings not documented with administrative codes using natural language processing. AMIA Annu Symp Proc 2018; 2017: 1923-1930
• 40 Osheroff J, Teich J, Levick D. , et al. Improving outcomes with clinical decision support: an implementer's guide. 2nd ed. Chicago, IL: HIMSS Publishing; 2012. . Doi: 10.4324/9780367806125