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.

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Clinical Quality and Patient Experience in the Adult Ambulatory Setting

American Journal of Medical Quality ◽

10.1177/1062860618777878 ◽

2018 ◽

Vol 34 (1) ◽

pp. 87-91 ◽

Cited By ~ 1

Author(s):

Susan Congiusta ◽

Philip Solomon ◽

Joseph Conigliaro ◽

Roseanne O’Gara-Shubinsky ◽

Nina Kohn ◽

...

Keyword(s):

Health System ◽

Patient Experience ◽

Care Delivery ◽

Quality Measures ◽

Outpatient Setting ◽

Ambulatory Setting ◽

Clinical Quality ◽

Composite Measures ◽

P 75 ◽

Adult Outpatient

Quality and patient experience are important dimensions of care delivery. The extent to which they are related in the adult outpatient setting is unknown. This brief study utilized data from a large integrated health system over a 1-year period in 2015 and measured the degree of correlation between physicians’ patient experience scores and 8 standardized quality metrics. These quality measures were paired into similar groups to create 4 composite measures: outcome, screening, vaccination, and adherence. Measures of outcome ( r = 0.20, P = .06), vaccination ( r = 0.12, P = .26), and adherence ( r = −0.04, P = .75) were not significantly correlated with patient experience; screening ( r = 0.29, P = .006) was minimally correlated with patient experience. Overall, this study found minimal correlation between measures of patient experience and clinical quality in the outpatient setting. Measurement of both of these domains is essential to understanding patterns of care.

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Assessment and Stratification of High-Impact Data Elements in Electronic Clinical Quality Measures: A Joint Data Quality Initiative Between CancerLinQ® and Cancer Treatment Centers of America

JCO Clinical Cancer Informatics ◽

10.1200/cci.17.00139 ◽

2018 ◽

pp. 1-10

Author(s):

Rory J. Lettvin ◽

Alpna Wayal ◽

Amy McNutt ◽

Robert S. Miller ◽

Robert Hauser

Keyword(s):

Data Quality ◽

Cancer Treatment ◽

Quality Measures ◽

Potential Effect ◽

High Impact ◽

Specific Data ◽

Clinical Quality ◽

Data Elements ◽

Clinical Measures ◽

Impact Data

Purpose A joint data quality initiative between the Cancer Treatment Centers of America and the ASCO big data health technology platform CancerLinQ® was initiated to document and codify the steps taken to evaluate, stratify, and determine the potential effect of data elements used for electronic clinical quality measures as captured within structured fields in electronic health records. Methods The processes involved the identification of clinical concepts required in measure population criteria and then to map these to the corresponding components of the CancerLinQ data model. A quantitative assessment of mappings between electronic clinical quality measure clinical concepts and attributes from the CancerLinQ clinical database was performed. In parallel, a qualitative analysis of high-impact data elements from the Cancer Treatment Centers of America clinical measures was made using local, expert consensus. Results An impact assessment was derived using a count of the data elements across measures and the specific population criteria affected. Conclusion A list of putative high-impact data elements can provide guidance for clinicians to facilitate specific data element capture related to quality metrics in an electronic environment.

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Challenges and Opportunities in Delivering High-Quality Cancer Care: A 2016 Update

American Society of Clinical Oncology Educational Book ◽

10.1200/edbk_159303 ◽

2016 ◽

pp. e294-e300

Author(s):

Patricia A. Ganz ◽

Michael J. Hassett ◽

David C. Miller

Keyword(s):

Cancer Care ◽

Care Delivery ◽

Quality Measurement ◽

Quality Measures ◽

The United States ◽

Institute Of Medicine ◽

Patients With Cancer ◽

Quality Cancer Care ◽

Challenges And Opportunities ◽

Quality Improvement Collaboratives

Herein, both the rationale and scope of current initiatives aimed at improving the quality of cancer care delivery in the United States are described. First, we discuss a recent report from the Institute of Medicine that issued a strong call for both the development of quality measures in oncology and implementation of a learning health care system in which data and experience from clinical practice can inform continuous improvements in cancer care. Second, we review the multiple, diverse initiatives that are underway to identify, test, and validate quality measures for the entire spectrum of cancer care. Finally, we discuss regional quality improvement collaboratives as one successful approach to creating a cycle of quality measurement, identification of best practices, and implementation of changes in practice patterns that ultimately yield improved care and outcomes for patients with cancer.

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Using a stakeholder-engaged approach to develop and validate electronic clinical quality measures

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocw137 ◽

2016 ◽

Vol 24 (3) ◽

pp. 503-512

Author(s):

Jill Boylston Herndon ◽

Krishna Aravamudhan ◽

Ronald L Stephenson ◽

Ryan Brandon ◽

Jesley Ruff ◽

...

Keyword(s):

Reliability And Validity ◽

Kappa Statistic ◽

Quality Measures ◽

The United States ◽

National Quality Forum ◽

Perfect Agreement ◽

Clinical Quality ◽

National Quality ◽

Test Feasibility ◽

Data Elements

Objective: To describe the stakeholder-engaged processes used to develop, specify, and validate 2 oral health care electronic clinical quality measures. Materials and Methods: A broad range of stakeholders were engaged from conception through testing to develop measures and test feasibility, reliability, and validity following National Quality Forum guidance. We assessed data element feasibility through semistructured interviews with key stakeholders using a National Quality Forum–recommended scorecard. We created test datasets of synthetic patients to test measure implementation feasibility and reliability within and across electronic health record (EHR) systems. We validated implementation with automated reporting of EHR clinical data against manual record reviews, using the kappa statistic. Results: A stakeholder workgroup was formed and guided all development and testing processes. All critical data elements passed feasibility testing. Four test datasets, representing 577 synthetic patients, were developed and implemented within EHR vendors’ software, demonstrating measure implementation feasibility. Measure reliability and validity were established through implementation at clinical practice sites, with kappa statistic values in the “almost perfect” agreement range of 0.80–0.99 for all but 1 measure component, which demonstrated “substantial” agreement. The 2 validated measures were published in the United States Health Information Knowledgebase. Conclusion: The stakeholder-engaged processes used in this study facilitated a successful measure development and testing cycle. Engaging stakeholders early and throughout development and testing promotes early identification of and attention to potential threats to feasibility, reliability, and validity, thereby averting significant resource investments that are unlikely to be fruitful.

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Don't Fear Clinical Quality Measures

Family Practice News ◽

10.1016/s0300-7073(12)70475-9 ◽

2012 ◽

Vol 42 (11) ◽

pp. 51

Author(s):

CHRISTOPHER NOTTE ◽

NEIL SKOLNIK

Keyword(s):

Quality Measures ◽

Clinical Quality

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Exploring the association of social determinants of health and clinical quality measures and performance in HRSA-funded health centres

Family Medicine and Community Health ◽

10.1136/fmch-2020-000853 ◽

2021 ◽

Vol 9 (3) ◽

pp. e000853

Author(s):

Michael Topmiller ◽

Jessica McCann ◽

Jennifer Rankin ◽

Hank Hoang ◽

Joshua Bolton ◽

...

Keyword(s):

Cancer Screening ◽

Social Determinants Of Health ◽

Social Determinants ◽

Social Deprivation ◽

Health Centre ◽

Quality Measures ◽

Service Area ◽

Determinants Of Health ◽

Clinical Quality ◽

Health Centres

ObjectiveThis paper explores the impact of service area-level social deprivation on health centre clinical quality measures.DesignCross-sectional data analysis of Health Resources and Services Administration (HRSA)-funded health centres. We created a weighted service area social deprivation score for HRSA-funded health centres as a proxy measure for social determinants of health, and then explored adjusted and unadjusted clinical quality measures by weighted service area Social Deprivation Index quartiles for health centres.SettingsHRSA-funded health centres in the USA.ParticipantsOur analysis included a subset of 1161 HRSA-funded health centres serving more than 22 million mostly low-income patients across the country.ResultsHigher levels of social deprivation are associated with statistically significant poorer outcomes for all clinical quality outcome measures (both unadjusted and adjusted), including rates of blood pressure control, uncontrolled diabetes and low birth weight. The adjusted and unadjusted results are mixed for clinical quality process measures as higher levels of social deprivation are associated with better quality for some measures including cervical cancer screening and child immunisation status but worse quality for other such as colorectal cancer screening and early entry into prenatal care.ConclusionsThis research highlights the importance of incorporating community characteristics when evaluating clinical outcomes. We also present an innovative method for capturing health centre service area-level social deprivation and exploring its relationship to health centre clinical quality measures.

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Information technology � Software measurement � Software quality measurement � Automated source code quality measures

10.3403/30412917u ◽

2021 ◽

Keyword(s):

Information Technology ◽

Software Quality ◽

Source Code ◽

Quality Measurement ◽

Quality Measures ◽

Software Measurement ◽

Measurement Software ◽

Code Quality ◽

Software Quality Measurement

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Engaging Multidisciplinary Stakeholders to Drive Shared Decision-Making in Oncology

Journal of Palliative Care ◽

10.1177/0825859718810723 ◽

2018 ◽

Vol 34 (1) ◽

pp. 29-31 ◽

Cited By ~ 6

Author(s):

Gabrielle Rocque ◽

Ellen Miller-Sonnet ◽

Alan Balch ◽

Carrie Stricker ◽

Josh Seidman ◽

...

Keyword(s):

Health Care ◽

Decision Making ◽

Best Practices ◽

Shared Decision Making ◽

Best Practice ◽

Care Delivery ◽

Evidence Based ◽

Shared Decision ◽

Oncology Practice ◽

Development And Validation

Although recognized as best practice, regular integration of shared decision-making (SDM) approaches between patients and oncologists remains an elusive goal. It is clear that usable, feasible, and practical tools are needed to drive increased SDM in oncology. To address this goal, we convened a multidisciplinary collaborative inclusive of experts across the health-care delivery ecosystem to identify key principles in designing and testing processes to promote SDM in routine oncology practice. In this commentary, we describe 3 best practices for addressing challenges associated with implementing SDM that emerged from a multidisciplinary collaborative: (1) engagement of diverse stakeholders who have interest in SDM, (2) development and validation of an evidence-based SDM tool grounded within an established conceptual framework, and (3) development of the necessary roadmap and consideration of the infrastructure needed for engendering patient engagement in decision-making. We believe these 3 principles are critical to the success of creating SDM tools to be utilized both within and outside of clinical practice. We are optimistic that shared use across settings will support adoption of this tool and overcome barriers to implementing SDM within busy clinical workflows. Ultimately, we hope that this work will offer new perspectives on what is important to patients and provide an important impetus for leveraging patient preferences and values in decision-making.

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