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

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.

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

2020 ◽  
Vol 11 (01) ◽  
pp. 023-033
Author(s):  
Robert C. McClure ◽  
Caroline L. Macumber ◽  
Julia L. Skapik ◽  
Anne Marie Smith
Keyword(s):  
Care Delivery ◽  
Quality Measurement ◽  
Quality Measures ◽  
Data Representation ◽  
Evidence Based ◽  
Data Mapping ◽  
Local Data ◽  
Clinical Quality ◽  
Care Gaps ◽  
Data Elements

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.

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

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.

A Review of Failure Handling Mechanisms for Data Quality Measures

2012 ◽  
Author(s):  
Nurul A. Emran ◽  
Noraswaliza Abdullah ◽  
Nuzaimah Mustafa
Keyword(s):  
Data Quality ◽  
Quality Measures ◽  
Failure Handling

Don't Fear Clinical Quality Measures

2012 ◽  
Vol 42 (11) ◽  
pp. 51
Author(s):  
CHRISTOPHER NOTTE ◽  
NEIL SKOLNIK
Keyword(s):  
Quality Measures ◽  
Clinical Quality

scholarly journals Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data

2021 ◽  
Vol 11 (2) ◽  
pp. 472
Author(s):  
Hyeongmin Cho ◽  
Sangkyun Lee
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Large Scale ◽  
High Dimensional Data ◽  
Quality Measures ◽  
Training Data ◽  
Measure Data ◽  
High Dimensional ◽  
Small Scale ◽  
Class Separability

Machine learning has been proven to be effective in various application areas, such as object and speech recognition on mobile systems. Since a critical key to machine learning success is the availability of large training data, many datasets are being disclosed and published online. From a data consumer or manager point of view, measuring data quality is an important first step in the learning process. We need to determine which datasets to use, update, and maintain. However, not many practical ways to measure data quality are available today, especially when it comes to large-scale high-dimensional data, such as images and videos. This paper proposes two data quality measures that can compute class separability and in-class variability, the two important aspects of data quality, for a given dataset. Classical data quality measures tend to focus only on class separability; however, we suggest that in-class variability is another important data quality factor. We provide efficient algorithms to compute our quality measures based on random projections and bootstrapping with statistical benefits on large-scale high-dimensional data. In experiments, we show that our measures are compatible with classical measures on small-scale data and can be computed much more efficiently on large-scale high-dimensional datasets.

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

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.

scholarly journals Gender disparity between authors in leading medical journals during the COVID-19 pandemic: a cross-sectional review

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e051224
Author(s):  
Vaidehi Misra ◽  
Frozan Safi ◽  
Kathryn A Brewerton ◽  
Wei Wu ◽  
Robin Mason ◽  
...  
Keyword(s):  
Data Extraction ◽  
Gender Disparity ◽  
High Impact ◽  
Original Research ◽  
Eligibility Criteria ◽  
Cross Sectional ◽  
Medical Journals ◽  
The Usa ◽  
Study Characteristics ◽  
Data Elements

ObjectivesEvaluate gender differences in authorship of COVID-19 articles in high-impact medical journals compared with other topics.DesignCross-sectional review.Data sourcesMedline database.Eligibility criteriaArticles published from 1 January to 31 December 2020 in the seven leading general medical journals by impact factor. Article types included primary research, reviews, editorials and commentaries.Data extractionKey data elements were whether the study topic was related to COVID-19 and names of the principal and the senior authors. A hierarchical approach was used to determine the likely gender of authors. Logistic regression assessed the association of study characteristics, including COVID-19 status, with authors’ likely gender; this was quantified using adjusted ORs (aORs).ResultsWe included 2252 articles, of which 748 (33.2%) were COVID-19-related and 1504 (66.8%) covered other topics. A likely gender was determined for 2138 (94.9%) principal authors and 1890 (83.9%) senior authors. Men were significantly more likely to be both principal (1364 men; 63.8%) and senior (1332 men; 70.5%) authors. COVID-19-related articles were not associated with the odds of men being principal (aOR 0.99; 95% CI 0.81 to 1.21; p=0.89) or senior authors (aOR 0.96; 95% CI 0.78 to 1.19; p=0.71) relative to other topics. Articles with men as senior authors were more likely to have men as principal authors (aOR 1.49; 95% CI 1.21 to 1.83; p<0.001). Men were more likely to author articles reporting original research and those with corresponding authors based outside the USA and Europe.ConclusionsWomen were substantially under-represented as authors among articles in leading medical journals; this was not significantly different for COVID-19-related articles. Study limitations include potential for misclassification bias due to the name-based analysis. Results suggest that barriers to women’s authorship in high-impact journals during COVID-19 are not significantly larger than barriers that preceded the pandemic and that are likely to continue beyond it.PROSPERO registration numberCRD42020186702.

Data quality measures based on granular computing for multi-label classification

2021 ◽  
Vol 560 ◽  
pp. 51-67
Author(s):  
Marilyn Bello ◽  
Gonzalo Nápoles ◽  
Koen Vanhoof ◽  
Rafael Bello
Keyword(s):  
Data Quality ◽  
Granular Computing ◽  
Quality Measures

Performance on Electronic Clinical Quality Measures in the Comprehensive Primary Care Initiative

2018 ◽  
Vol 34 (2) ◽  
pp. 119-126
Author(s):  
Christiane T. LaBonte ◽  
Perry Payne ◽  
William Rollow ◽  
Mark W. Smith ◽  
Abdul Nissar ◽  
...  
Keyword(s):  
Primary Care ◽  
Quality Measures ◽  
Clinical Quality

Big data to knowledge – Harnessing semiotic relationships of data quality and skills in genome curation work

2018 ◽  
Vol 44 (6) ◽  
pp. 785-801
Author(s):  
Hong Huang
Keyword(s):  
Quality Assurance ◽  
Big Data ◽  
Data Quality ◽  
Strong Support ◽  
Policy Makers ◽  
Specific Data ◽  
Theoretical Perspectives ◽  
Funding Agencies ◽  
Quality Dimensions ◽  
Data Quality Dimensions

This article aims to understand the views of genomic scientists with regard to the data quality assurances associated with semiotics and data–information–knowledge (DIK). The resulting communication of signs generated from genomic curation work, was found within different semantic levels of DIK that correlate specific data quality dimensions with their respective skills. Syntactic data quality dimensions were ranked the highest among all other semiotic data quality dimensions, which indicated that scientists spend great efforts for handling data wrangling activities in genome curation work. Semantic- and pragmatic-related sign communications were about meaningful interpretation, thus required additional adaptive and interpretative skills to deal with data quality issues. This expanded concept of ‘curation’ as sign/semiotic was not previously explored from the practical to the theoretical perspectives. The findings inform policy makers and practitioners to develop framework and cyberinfrastructure that facilitate the initiatives and advocacies of ‘Big Data to Knowledge’ by funding agencies. The findings from this study can also help plan data quality assurance policies and thus maximise the efficiency of genomic data management. Our results give strong support to the relevance of data quality skills communication for relationship with data quality assurance in genome curation activities.

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