scholarly journals Assessing the quality of administrative data for research: a framework from the Manitoba Centre for Health Policy

2017 ◽  
Vol 25 (3) ◽  
pp. 224-229 ◽  
Author(s):  
Mark Smith ◽  
Lisa M Lix ◽  
Mahmoud Azimaee ◽  
Jennifer E Enns ◽  
Justine Orr ◽  
...  
Keyword(s):  
Health Policy ◽  
Best Practices ◽  
Data Quality ◽  
Administrative Data ◽  
Research Literature ◽  
Data Repositories ◽  
The Core ◽  
Data Quality Assessment ◽  
Quality Framework

Abstract The growth of administrative data repositories worldwide has spurred the development and application of data quality frameworks to ensure that research analyses based on these data can be used to draw meaningful conclusions. However, the research literature on administrative data quality is sparse, and there is little consensus regarding which dimensions of data quality should be measured. Here we present the core dimensions of the data quality framework developed at the Manitoba Centre for Health Policy, a world leader in the use of administrative data for research purposes, and provide examples and context for the application of these dimensions to conducting data quality evaluations. In sharing this framework, our ultimate aim is to promote best practices in rigorous data quality assessment among users of administrative data for research.

scholarly journals Quality Assessment of Imputations in Administrative Data

2015 ◽  
Vol 31 (2) ◽  
pp. 231-247 ◽  
Author(s):  
Matthias Schnetzer ◽  
Franz Astleithner ◽  
Predrag Cetkovic ◽  
Stefan Humer ◽  
Manuela Lenk ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Quality ◽  
Quality Assessment ◽  
Administrative Data ◽  
Raw Data ◽  
Computational Approaches ◽  
Data Level

Abstract This article contributes a framework for the quality assessment of imputations within a broader structure to evaluate the quality of register-based data. Four quality-related hyperdimensions examine the data processing from the raw-data level to the final statistics. Our focus lies on the quality assessment of different imputation steps and their influence on overall data quality. We suggest classification rates as a measure of accuracy of imputation and derive several computational approaches.

scholarly journals Practical Application of a Data Stewardship Maturity Matrix for the NOAA OneStop Project

2018 ◽  
Author(s):  
Ge Peng ◽  
Anna Milan ◽  
Nancy A. Ritchey ◽  
Robert P. Partee ◽  
Sonny Zinn ◽  
...  
Keyword(s):  
North Carolina ◽  
Best Practices ◽  
Data Quality ◽  
User Needs ◽  
Data Quality Control ◽  
Practical Application ◽  
Data Discovery ◽  
Data Quality Assessment ◽  
Data Stewardship ◽  
Do So

Assessing the stewardship maturity of individual datasets is an essential part of ensuring and improving the way datasets are documented, preserved, and disseminated to users. It is a critical step towards meeting U.S. federal regulations, organizational requirements, and user needs. However, it is challenging to do so consistently and quantifiably. The Data Stewardship Maturity Matrix (DSMM), developed jointly by NOAA’s National Centers for Environmental Information (NCEI) and the Cooperative Institute for Climate and Satellites–North Carolina (CICS-NC), provides a uniform framework for consistently rating stewardship maturity of individual datasets in nine key components: preservability, accessibility, usability, production sustainability, data quality assurance, data quality control/monitoring, data quality assessment, transparency/traceability, and data integrity. So far, the DSMM has been applied to over 900 individual datasets that are archived and/or managed by NCEI, in support of the NOAA’s OneStop Data Discovery and Access Framework Project. As a part of the OneStop-ready process, tools, implementation guidance, workflows, and best practices are developed to assist the application of the DSMM and described in this paper. The DSMM ratings are also consistently captured in the ISO standard-based dataset-level quality metadata and citable quality descriptive information documents, which serve as interoperable quality information to both machine and human end-users. These DSMM implementation and integration workflows and best practices could be adopted by other data management and stewardship projects or adapted for applications of other maturity assessment models.

scholarly journals Data Quality: A Negotiator between Paper-based and Digital Records in the Pakistan’s TB Control Program

2018 ◽  
Author(s):  
Syed Mustafa Ali ◽  
Farah Naureen ◽  
Arif Noor ◽  
Maged Kamel N. Boulos ◽  
Javariya Aamir ◽  
...  
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Control Program ◽  
Digital Data ◽  
Patient Treatment ◽  
Assessment Framework ◽  
Healthcare Organizations ◽  
Data Quality Assessment ◽  
Quality Issues

Background Increasingly, healthcare organizations are using technology for the efficient management of data. The aim of this study was to compare the data quality of digital records with the quality of the corresponding paper-based records by using data quality assessment framework. Methodology We conducted a desk review of paper-based and digital records over the study duration from April 2016 to July 2016 at six enrolled TB clinics. We input all data fields of the patient treatment (TB01) card into a spreadsheet-based template to undertake a field-to-field comparison of the shared fields between TB01 and digital data. Findings A total of 117 TB01 cards were prepared at six enrolled sites, whereas just 50% of the records (n=59; 59 out of 117 TB01 cards) were digitized. There were 1,239 comparable data fields, out of which 65% (n=803) were correctly matched between paper based and digital records. However, 35% of the data fields (n=436) had anomalies, either in paper-based records or in digital records. 1.9 data quality issues were calculated per digital patient record, whereas it was 2.1 issues per record for paper-based record. Based on the analysis of valid data quality issues, it was found that there were more data quality issues in paper-based records (n=123) than in digital records (n=110). Conclusion There were fewer data quality issues in digital records as compared to the corresponding paper-based records. Greater use of mobile data capture and continued use of the data quality assessment framework can deliver more meaningful information for decision making.

Data Quality in an Information-Rich Environment: Canada as an Example

2005 ◽  
Vol 24 (S1) ◽  
pp. 153-170 ◽  
Author(s):  
Leslie L. Roos ◽  
Sumit Gupta ◽  
Ruth-Ann Soodeen ◽  
Laurel Jebamani
Keyword(s):  
Data Quality ◽  
Administrative Data ◽  
Prescription Drugs ◽  
Record Linkage ◽  
Chronic Conditions ◽  
Quality Measurement ◽  
Primary Data ◽  
Canadian Provinces

ABSTRACTThis review evaluates the quality of available administrative data in the Canadian provinces, emphasizing the information needed to create integrated systems. We explicitly compare approaches to quality measurement, indicating where record linkage can and cannot substitute for more expensive record re-abstraction. Forty-nine original studies evaluating Canadian administrative data (registries, hospital abstracts, physician claims, and prescription drugs) are summarized in a structured manner. Registries, hospital abstracts, and physician files appear to be generally of satisfactory quality, though much work remains to be done. Data quality did not vary systematically among provinces. Primary data collection to check place of residence and longitudinal follow-up in provincial registries is needed. Promising initial checks of pharmaceutical data should be expanded. Because record linkage studies were “conservative” in reporting reliability, the reduction of time-consuming record re-abstraction appears feasible in many cases. Finally, expanding the scope of administrative data to study health, as well as health care, seems possible for some chronic conditions. The research potential of the information-rich environments being created highlights the importance of data quality.

scholarly journals Data Quality Issues With Physician-Rating Websites: Systematic Review (Preprint)

2019 ◽  
Author(s):  
Pavankumar Mulgund ◽  
Raj Sharman ◽  
Priya Anand ◽  
Shashank Shekhar ◽  
Priya Karadi
Keyword(s):  
Data Quality ◽  
English Language ◽  
Quality Of Data ◽  
Contextual Data ◽  
Patient Reported ◽  
Physician Rating ◽  
Quality Framework ◽  
Quality Issues ◽  
The Impact

BACKGROUND In recent years, online physician-rating websites have become prominent and exert considerable influence on patients’ decisions. However, the quality of these decisions depends on the quality of data that these systems collect. Thus, there is a need to examine the various data quality issues with physician-rating websites. OBJECTIVE This study’s objective was to identify and categorize the data quality issues afflicting physician-rating websites by reviewing the literature on online patient-reported physician ratings and reviews. METHODS We performed a systematic literature search in ACM Digital Library, EBSCO, Springer, PubMed, and Google Scholar. The search was limited to quantitative, qualitative, and mixed-method papers published in the English language from 2001 to 2020. RESULTS A total of 423 articles were screened. From these, 49 papers describing 18 unique data quality issues afflicting physician-rating websites were included. Using a data quality framework, we classified these issues into the following four categories: intrinsic, contextual, representational, and accessible. Among the papers, 53% (26/49) reported intrinsic data quality errors, 61% (30/49) highlighted contextual data quality issues, 8% (4/49) discussed representational data quality issues, and 27% (13/49) emphasized accessibility data quality. More than half the papers discussed multiple categories of data quality issues. CONCLUSIONS The results from this review demonstrate the presence of a range of data quality issues. While intrinsic and contextual factors have been well-researched, accessibility and representational issues warrant more attention from researchers, as well as practitioners. In particular, representational factors, such as the impact of inline advertisements and the positioning of positive reviews on the first few pages, are usually deliberate and result from the business model of physician-rating websites. The impact of these factors on data quality has not been addressed adequately and requires further investigation.

scholarly journals Canadian Approaches to Optimizing Quality of Administrative Data for Health System Use, Research, and Linkage

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Catherine Eastwood ◽  
Keith Denny ◽  
Maureen Kelly ◽  
Hude Quan
Keyword(s):  
Data Collection ◽  
Data Quality ◽  
Quality Assessment ◽  
Administrative Data ◽  
Information Quality ◽  
Health Data ◽  
Data Sources ◽  
University Of Calgary ◽  
Quality Assessment And Improvement

Theme: Data and Linkage QualityObjectives: To define health data quality from clinical, data science, and health system perspectives To describe some of the international best practices related to quality and how they are being applied to Canada’s administrative health data. To compare methods for health data quality assessment and improvement in Canada (automated logical checks, chart quality indicators, reabstraction studies, coding manager perspectives) To highlight how data linkage can be used to provide new insights into the quality of original data sources To highlight current international initiatives for improving coded data quality including results from current ICD-11 field trials Dr. Keith Denny: Director of Clinical Data Standards and Quality, Canadian Insititute for Health Information (CIHI), Adjunct Research Professor, Carleton University, Ottawa, ON. He provides leadership for CIHI’s information quality initiatives and for the development and application of clinical classifications and terminology standards. Maureen Kelly: Manager of Information Quality at CIHI, Ottawa, ON. She leads CIHI’s corporate quality program that is focused on enhancing the quality of CIHI’s data sources and information products and to fostering CIHI’s quality culture. Dr. Cathy Eastwood: Scientific Manager, Associate Director of Alberta SPOR Methods & Development Platform, Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB. She has expertise in clinical data collection, evaluation of local and systemic data quality issues, disease classification coding with ICD-10 and ICD-11. Dr. Hude Quan: Professor, Community Health Sciences, Cumming School of Medicine, University of Calgary, Director Alberta SPOR Methods Platform; Co-Chair of Hypertension Canada, Co-Chair of Person to Population Health Collaborative of the Libin Cardiovascular Institute in Calgary, AB. He has expertise in assessing, validating, and linking administrative data sources for conducting data science research including artificial intelligence methods for evaluating and improving data quality. Intended Outcomes:“What is quality health data?” The panel of experts will address this common question by discussing how to define high quality health data, and measures being taken to ensure that they are available in Canada. Optimizing the quality of clinical-administrative data, and their use-value, first requires an understanding of the processes used to create the data. Subsequently, we can address the limitations in data collection and use these data for diverse applications. Current advances in digital data collection are providing more solutions to improve health data quality at lower cost. This panel will describe a number of quality assessment and improvement initiatives aimed at ensuring that health data are fit for a range of secondary uses including data linkage. It will also discuss how the need for the linkage and integration of data sources can influence the views of the data source’s fitness for use. CIHI content will include: Methods for optimizing the value of clinical-administrative data CIHI Information Quality Framework Reabstraction studies (e.g. physician documentation/coders’ experiences) Linkage analytics for data quality University of Calgary content will include: Defining/measuring health data quality Automated methods for quality assessment and improvement ICD-11 features and coding practices Electronic health record initiatives

Traditional vs. Machine-Learning Techniques for OSM Quality Assessment

2019 ◽  
pp. 469-487
Author(s):  
Musfira Jilani ◽  
Michela Bertolotto ◽  
Padraig Corcoran ◽  
Amerah Alghanim
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Quality Assessment ◽  
Spatial Data ◽  
Current Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Data Quality Assessment ◽  
Expensive Process

Nowadays an ever-increasing number of applications require complete and up-to-date spatial data, in particular maps. However, mapping is an expensive process and the vastness and dynamics of our world usually render centralized and authoritative maps outdated and incomplete. In this context crowd-sourced maps have the potential to provide a complete, up-to-date, and free representation of our world. However, the proliferation of such maps largely remains limited due to concerns about their data quality. While most of the current data quality assessment mechanisms for such maps require referencing to authoritative maps, we argue that such referencing of a crowd-sourced spatial database is ineffective. Instead we focus on the use of machine learning techniques that we believe have the potential to not only allow the assessment but also to recommend the improvement of the quality of crowd-sourced maps without referencing to external databases. This chapter gives an overview of these approaches.

Improving Data Quality in Face-to-Face Survey Research

2019 ◽  
Vol 53 (1) ◽  
pp. 46-50
Author(s):  
Carolyn Logan ◽  
Pablo Parás ◽  
Michael Robbins ◽  
Elizabeth J. Zechmeister
Keyword(s):  
Best Practices ◽  
Data Quality ◽  
Political Science ◽  
Survey Research ◽  
Survey Data ◽  
Political Behavior ◽  
High Quality ◽  
Face To Face ◽  
The World

ABSTRACTData quality in survey research remains a paramount concern for those studying mass political behavior. Because surveys are conducted in increasingly diverse contexts around the world, ensuring that best practices are followed becomes ever more important to the field of political science. Bringing together insights from surveys conducted in more than 80 countries worldwide, this article highlights common challenges faced in survey research and outlines steps that researchers can take to improve the quality of survey data. Importantly, the article demonstrates that with the investment of the necessary time and resources, it is possible to carry out high-quality survey research even in challenging environments in which survey research is not well established.

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