Lessons Learned in Creating Interoperable Fast Healthcare Interoperability Resources Profiles for Large-Scale Public Health Programs

Matney, Susan; Heale, Bret S.E.; Hasley, Steve; Decker, Emily; Frederiksen, Brittni N.; Davis, Nathan; Langford, Patrick; Ramey, Nadia; Huff, Stanley M.

doi:10.1055/s-0038-1677527

Cited by 17 publications

(12 citation statements)

References 15 publications

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“…The harmonization work was executed in two phases, the first of which focused on harmonizing the semantic meaning of measures across all the 28 standard-sets published to date. In the second phase, measures corresponding to four pilot conditions [Breast cancer ( 31 ), Cataracts ( 32 ), Inflammatory bowel disease ( 33 ), and Heart failure ( 34 )] were expressed as logical models and mapped to reference terminologies in a pilot study, which is expected to further facilitate data collection and analysis ( 35 ).…”

Section: Methodsmentioning

confidence: 99%

Harmonization of the ICHOM Quality Measures to Enable Health Outcomes Measurement in Multimorbid Patients

Blom¹,

Khalid²,

Van-Lettow

et al. 2020

Front. Digit. Health

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Objectives: To update the sets of patient-centric outcomes measures (“standard-sets”) developed by the not-for-profit organization ICHOM to become more readily applicable in patients with multimorbidity and to facilitate their implementation in health information systems. To that end we set out to (i) harmonize measures previously defined separately for different conditions, (ii) create clinical information models from the measures, and (iii) restructure the annotation to make the sets machine-readable.Materials and Methods: First, we harmonized the semantic meaning of individual measures across all the 28 standard-sets published to date, in a harmonized measure repository. Second, measures corresponding to four conditions (Breast cancer, Cataracts, Inflammatory bowel disease and Heart failure) were expressed as logical models and mapped to reference terminologies in a pilot study.Results: The harmonization of semantic meaning resulted in a consolidation of measures used across the standard-sets by 15%, from 3,178 to 2,712. These were all converted into a machine-readable format. 61% of the measures in the 4 pilot sets were bound to existing concepts in either SNOMED CT or LOINC.Discussion: The harmonization of ICHOM measures across conditions is expected to increase the applicability of ICHOM standard-sets to multi-morbid patients, as well as facilitate their implementation in health information systems.Conclusion: Harmonizing the ICHOM measures and making them machine-readable is expected to expedite the global adoption of systematic and interoperable outcomes measurement. In turn, we hope that the improved transparency on health outcomes that follows will let health systems across the globe learn from each other to the ultimate benefit of patients.

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Section: Methodsmentioning

confidence: 99%

Harmonization of the ICHOM Quality Measures to Enable Health Outcomes Measurement in Multimorbid Patients

Blom¹,

Khalid²,

Van-Lettow

et al. 2020

Front. Digit. Health

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“…28,[33][34][35][36][37] Similarly, pilot-testing standards may help identify implementation challenges that must be addressed prior to implementation and may help advance a given standard's maturity. 38 In this article, we summarize the approach ONC has employed to advance standards development through pilot-testing to serve as a guide for those seeking to evaluate new standards through pilot projects.…”

Section: Background and Significancementioning

confidence: 99%

Fast-Tracking Health Data Standards Development and Adoption in Real-World Settings: A Pilot Approach

2021

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Background Pilot-testing is important in standards development because it facilitates agile navigation of the gap between needs for and use of standards in real-world settings and can reveal the practicalities of implementation. As the implementation and use of health data standards are usually more complicated than anticipated, the Office of the National Coordinator for Health Information Technology (ONC) routinely oversees and organizes relevant pilot projects. Objectives This article provides an in-depth look into a sample of ONC's standards-focused pilot projects to (1) inform readers of the complexities of developing, implementing, and advancing standards and (2) guide those seeking to evaluate new standards through pilot projects. Methods The ONC's approach to conducting pilot projects begins with identifying a clinical care need, research requirement, or policy outcome that is not well supported by existing standards through a landscape review. ONC then selects a testing approach based on the identified need and maturity of relevant standards. Next, ONC identifies use cases and sites to pilot-test the relevant standard. Once complete, ONC publishes a report that informs subsequent projects and standards development. Results Pilot projects presented here are organized into three categories related to their demonstrated focus and related approach: (1) improving standards for presenting and sharing clinical genetic data, (2) accelerating the development and implementation of new standards, and (3) facilitating clinical data reuse. Each project illustrates the pilot approach from inception to next steps, capturing the role of collaboration among standards development organizations, stakeholders, and end-users to ensure standards are practical and fit for purpose. Conclusion The ONC approach identifies implementation difficulties prior to broader adoption and use of standards, and provides insight into the steps needed to scale use of standards. The ONC's organization of pilot projects serves as a natural accelerator for building communities of practice, often providing a well-connected beneficiary of lessons learned.

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“…Even with the QDM as a library for data element choices, variability continues to exist among quality measures for implementation because some measures do not use the QDM and other newer models continue to evolve that capture the interest and resources of the measure community. With the exploding interest in HL7 FHIR, 29 30 there is ongoing work to align QDM with FHIR representations of clinical artifacts occurring through, as mentioned previously, the movement to adopt the CQL 31 32 within the US CMS program use of eCQMs.…”

Section: Impact On Implementation and Clinical Practicementioning

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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Lessons Learned in Creating Interoperable Fast Healthcare Interoperability Resources Profiles for Large-Scale Public Health Programs

Cited by 17 publications

References 15 publications

Harmonization of the ICHOM Quality Measures to Enable Health Outcomes Measurement in Multimorbid Patients

Harmonization of the ICHOM Quality Measures to Enable Health Outcomes Measurement in Multimorbid Patients

Fast-Tracking Health Data Standards Development and Adoption in Real-World Settings: A Pilot Approach

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

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