Using Clinical Data Standards to Measure Quality: A New Approach

D'Amore, John D.; Li, Chun; McCrary, Laura; Niloff, Jonathan M.; Sittig, Dean F.; McCoy, Allison B.; Wright, Adam

doi:10.1055/s-0038-1656548

Cited by 17 publications

(28 citation statements)

References 18 publications

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“…Similarly, construction of the value set and performing quality assurance are correspondingly difficult and labor-intensive. Inaccuracies in value sets can significantly affect clinical quality measure calculations [15].…”

Section: Introductionmentioning

confidence: 99%

SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets

et al. 2019

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BackgroundDefining clinical phenotypes from electronic health record (EHR)–derived data proves crucial for clinical decision support, population health endeavors, and translational research. EHR diagnoses now commonly draw from a finely grained clinical terminology—either native SNOMED CT or a vendor-supplied terminology mapped to SNOMED CT concepts as the standard for EHR interoperability. Accordingly, electronic clinical quality measures (eCQMs) increasingly define clinical phenotypes with SNOMED CT value sets. The work of creating and maintaining list-based value sets proves daunting, as does insuring that their contents accurately represent the clinically intended condition.ObjectiveThe goal of the research was to compare an intensional (concept hierarchy-based) versus extensional (list-based) value set approach to defining clinical phenotypes using SNOMED CT–encoded data from EHRs by evaluating value set conciseness, time to create, and completeness.MethodsStarting from published Centers for Medicare and Medicaid Services (CMS) high-priority eCQMs, we selected 10 clinical conditions referenced by those eCQMs. For each, the published SNOMED CT list-based (extensional) value set was downloaded from the Value Set Authority Center (VSAC). Ten corresponding SNOMED CT hierarchy-based intensional value sets for the same conditions were identified within our EHR. From each hierarchy-based intensional value set, an exactly equivalent full extensional value set was derived enumerating all included descendant SNOMED CT concepts. Comparisons were then made between (1) VSAC-downloaded list-based (extensional) value sets, (2) corresponding hierarchy-based intensional value sets for the same conditions, and (3) derived list-based (extensional) value sets exactly equivalent to the hierarchy-based intensional value sets. Value set conciseness was assessed by the number of SNOMED CT concepts needed for definition. Time to construct the value sets for local use was measured. Value set completeness was assessed by comparing contents of the downloaded extensional versus intensional value sets. Two measures of content completeness were made: for individual SNOMED CT concepts and for the mapped diagnosis clinical terms available for selection within the EHR by clinicians.ResultsThe 10 hierarchy-based intensional value sets proved far simpler and faster to construct than exactly equivalent derived extensional value set lists, requiring a median 3 versus 78 concepts to define and 5 versus 37 minutes to build. The hierarchy-based intensional value sets also proved more complete: in comparison, the 10 downloaded 2018 extensional value sets contained a median of just 35% of the intensional value sets’ SNOMED CT concepts and 65% of mapped EHR clinical terms.ConclusionsIn the EHR era, defining conditions preferentially should employ SNOMED CT concept hierarchy-based (intensional) value sets rather than extensional lists. By doing so, clinical guideline and eCQM authors can more readily engage specialists in vetting condition subtypes to i...

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Section: Introductionmentioning

confidence: 99%

SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets

et al. 2019

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“…All selected measures are eligible for MIPS eCQM reporting and many have been profiled in previous research. 9 , 10 , 32–34 …”

Section: Methodsmentioning

confidence: 99%

Clinical data sharing improves quality measurement and patient safety

D'Amore

McCrary

Denson

et al. 2021

Journal of the American Medical Informatics Association

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Objective Accurate and robust quality measurement is critical to the future of value-based care. Having incomplete information when calculating quality measures can cause inaccuracies in reported patient outcomes. This research examines how quality calculations vary when using data from an individual electronic health record (EHR) and longitudinal data from a health information exchange (HIE) operating as a multisource registry for quality measurement. Materials and Methods Data were sampled from 53 healthcare organizations in 2018. Organizations represented both ambulatory care practices and health systems participating in the state of Kansas HIE. Fourteen ambulatory quality measures for 5300 patients were calculated using the data from an individual EHR source and contrasted to calculations when HIE data were added to locally recorded data. Results A total of 79% of patients received care at more than 1 facility during the 2018 calendar year. A total of 12 994 applicable quality measure calculations were compared using data from the originating organization vs longitudinal data from the HIE. A total of 15% of all quality measure calculations changed (P < .001) when including HIE data sources, affecting 19% of patients. Changes in quality measure calculations were observed across measures and organizations. Discussion These results demonstrate that quality measures calculated using single-site EHR data may be limited by incomplete information. Effective data sharing significantly changes quality calculations, which affect healthcare payments, patient safety, and care quality. Conclusions Federal, state, and commercial programs that use quality measurement as part of reimbursement could promote more accurate and representative quality measurement through methods that increase clinical data sharing.

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“…The inability to easily share, implement, and extract data for electronic clinical quality measurement remains a major barrier to the successful execution of standardized, cost-effective, and accurate quality measurement. 6,7 The eCQMs intend to use point-of-care electronic data to trigger actions and evaluate adherence to evidence-based guidelines at the individual patient level. Given that we want to improve quality, eCQMs should align with clinical decision support (CDS) applications and address these same care gaps in real time.…”

Section: Background and Significancementioning

confidence: 99%

Section: Background and Significancementioning

confidence: 99%

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

McClure¹,

Macumber²,

Skapik

et al. 2020

Appl Clin Inform

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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.

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Using Clinical Data Standards to Measure Quality: A New Approach

Cited by 17 publications

References 18 publications

SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets

SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets

Clinical data sharing improves quality measurement and patient safety

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

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