scholarly journals Constructing a Toolkit to Evaluate Quality of State and Local Administrative Data

2018 ◽  
Vol 3 (5) ◽  
Author(s):  
Zachary H. Seeskin ◽  
A. Rupa Datta ◽  
Gabriel Ugarte
Keyword(s):  
Data Quality ◽  
Administrative Data ◽  
Longitudinal Component ◽  
Administrative Databases ◽  
Critical Dimensions ◽  
Administrative Dataset ◽  
State And Local ◽  
Local Agencies ◽  
And Training

State and local agencies administering programs have in their administrative data a powerful resource for policy analysis to inform evaluation and guide improvement of their programs. Understanding different aspects of their administrative data quality is critical for agencies to conduct such analyses and to improve their data for future use. However, state and local agencies often lack the resources and training for staff to conduct rigorous evaluations of data quality. We describe our efforts developing tools that can be used to assess data quality as well as the challenges encountered in constructing these tools. The toolkit focuses on critical dimensions of quality for analyzing an administrative dataset, including checks on data accuracy, the completeness of the records, and the comparability of the data over time and among subgroups of interest. State and local administrative databases often include a longitudinal component which our toolkit also aims to exploit to help evaluate data quality. While we seek to develop general tools for common data quality analyses, most administrative datasets have particularities that can benefit from a customized analysis building on our toolkit. In addition, we incorporate data visualization to draw attention to sets of records or variables that contain outliers or for which quality may be a concern.

scholarly journals Constructing a toolkit to evaluate quality of state and local administrative data

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Zachary H. Seeskin ◽  
Gabriel Ugarte ◽  
A. Rupa Datta
Keyword(s):  
Data Quality ◽  
Administrative Data ◽  
The United States ◽  
Longitudinal Component ◽  
Administrative Databases ◽  
Critical Dimensions ◽  
Assistance Programs ◽  
Administrative Dataset ◽  
State And Local ◽  
Local Agencies

In the United States, state and local agencies administering government assistance programs have in their administrative data a powerful resource for policy analysis to inform evaluation and guide improvement of their programs. Understanding different aspects of their administrative data quality is critical for agencies to conduct such analyses and to improve their data for future use. However, state and local agencies often lack the resources and training for staff to conduct rigorous evaluations of data quality. We describe our efforts in developing tools that can be used to assess data quality as well as the challenges encountered in constructing these tools. The toolkit focuses on critical dimensions of quality for analyzing an administrative dataset, including checks on data accuracy, the completeness of the records, and the comparability of the data over time and among subgroups of interest. State and local administrative databases often include a longitudinal component which our toolkit also aims to exploit to help evaluate data quality. In addition, we incorporate data visualization to draw attention to sets of records or variables that contain outliers or for which quality may be a concern. While we seek to develop general tools for common data quality analyses, most administrative datasets have particularities that can benefit from a customized analysis building on our toolkit.

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 Use of Electronic Administrative Databases to Measure Quality Indicators of Breast Cancer Care: Experience of Five Regional Oncology Networks in Italy

2020 ◽  
Vol 16 (2) ◽  
pp. e211-e220 ◽  
Author(s):  
Valentina Guarneri ◽  
Paolo Pronzato ◽  
Oscar Bertetto ◽  
Fausto Roila ◽  
Gianni Amunni ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Quality Indicators ◽  
Administrative Data ◽  
Population Level ◽  
Administrative Databases ◽  
Regional Variability ◽  
Care Experience ◽  
Italian Regions ◽  
Incident Cases

PURPOSE: Assuring quality of care, while maintaining sustainability, in complex conditions such as breast cancer (BC) is an important challenge for health systems. Here, we describe a methodology to define a set of quality indicators, assess their computability from administrative data, and apply them to a large cohort of BC cases. MATERIALS AND METHODS: Clinical professionals from the Italian Regional Oncology Networks identified 46 clinically relevant indicators of BC care; 22 were potentially computable using administrative data. Incident cases of BC diagnosed in 2016 in five Italian regions were identified using administrative databases from regional repositories. Each indicator was calculated through record linkage of anonymized individual data. RESULTS: A total of 15,342 incident BC cases were identified. Nine indicators were actually computable from administrative data (two structure and seven process indicators). Although most indicators were consistent with guidelines, for one indicator (blood tumor markers in the year after surgery, 44.2% to 64.5%; benchmark ≤ 20%), deviation was evident throughout the five regions, highlighting systematic overlooking of clinical recommendations. Two indicators (radiotherapy within 4 months after surgery if no adjuvant chemotherapy; 42% to 83.8%; benchmark ≥ 90%; and mammography 6 to 18 months after surgery, 55.1% to 72.6%; benchmark ≥ 90%) showed great regional variability and were lower than expected, possibly as result of an underestimation in indicator calculation by administrative data. CONCLUSION: Despite highlighting some limitations in the use of administrative data to measure health care performance, this study shows that evaluating the quality of BC care at a population level is possible and potentially useful for guiding quality improvement interventions.

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.

Reliability of administrative data for evaluating the quality of systemic treatment for cancer.

2017 ◽  
Vol 35 (8_suppl) ◽  
pp. 208-208 ◽  
Author(s):  
Melanie Lynn Powis ◽  
Nathan Taback ◽  
Christina Diong ◽  
Katherine Enright ◽  
Christopher M. Booth ◽  
...  
Keyword(s):  
Administrative Data ◽  
Gold Standard ◽  
Validation Cohort ◽  
Systemic Treatment ◽  
Early Stage ◽  
Quality Measures ◽  
Administrative Databases ◽  
Study Cohort ◽  
Reliability Characteristics

208 Background: There is ongoing interest in leveraging administrative data to examine quality but methodological concerns persist. We evaluated the reliability of a previously established panel of administrative data derived quality measures for systemic cancer treatment. Methods: The study cohort consisted of women diagnosed with early stage (stage I-III) breast cancer (ESBC) in Ontario, Canada, in 2010. Performance on 11 quality indicators evaluated using deterministically linked healthcare administrative databases has been reported previously. The sensitivity and specificity of these 11 indicators were examined using the chart as the gold standard. Results: The administrative cohort consisted of 6,795 women with ESBC from which a validation cohort of 705 patients was randomly selected from among patients who underwent cancer surgery at one of five hospitals chosen to balance feasibility and institutional characteristics.Sensitivity and specificity varied by indicator (Table). Reliability of some indicators may have been affected by suboptimal chart documentation in instances where care spanned multiple settings or the medical record was fragmented, or where the number of eligible patients for that indicator was low. Conclusions: Administrative data can be used to evaluate quality of systemic cancer therapy but understanding the reliability characteristics of individual indicators is essential to inform their appropriate use and interpretation. [Table: see text]

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

Quality indicators for end-of-life breast cancer care: Testing the use of administrative databases

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 6066-6066 ◽  
Author(s):  
L. Lethbridge ◽  
E. Grunfeld ◽  
R. Dewar ◽  
G. Johnston ◽  
P. McIntyre ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Quality Of Care ◽  
End Of Life ◽  
Administrative Data ◽  
Population Based ◽  
Vital Statistics ◽  
Administrative Databases ◽  
Breast Cancer Patients ◽  
Eol Care

6066 Background: Defining, measuring and monitoring quality of care is a facet of health services research that is growing in importance. Breast cancer offers a disease model to examine quality end-of-life (EOL) care provided to women. Administrative data have the unique potential to provide population-based measures of quality of care. The objective of this study was to assess the feasibility of using routinely-collected administrative data to measure quality EOL care for breast cancer patients. Methods: A cohort of all women in Nova Scotia who died of breast cancer between 01/01/1998 and 31/12/2002 was assembled from the Cancer Registry and Vital Statistics data. The EOL study period was defined as the last 6 months of life. A total of 864 women met the eligibility criteria. After a literature review, an expert panel identified 19 indicators that were potentially measurable through administrative data. Physician billings, hospital discharge abstracts and seniors pharmacare data, supplemented by clinical datasets, were utilized to calculate the statistics with which to represent the indicators. Results: Benchmark measures of care across the cohort show 63.4% died in a hospital, a mean continuity of care index of 0.786, and the mean number of inpatient days in the last 30 was 9.9. Indicators of aggressive care include 9.3% had chemotherapy in the last 14 days, 5.6% had more than 1 emergency room visit in the last 30 days, and 29.1% had more than 14 inpatient days in the last 30 days. Conclusions: Weaknesses of using these data include: 1) fixed variables with an administrative rather than a clinical objective; 2) lack of comprehensiveness of various datasets; and 3) the use of billings data where increasingly physicians are paid through methods other than fee-for-service. Strengths of this approach are: 1) population-based cohort; 2) comprehensiveness of cohort selection through the provincial Vital Statistics file; and 3) accessibility of data. No significant financial relationships to disclose.

Reliability of administrative data for evaluating quality of systemic cancer treatment.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e18269-e18269
Author(s):  
Monika K. Krzyzanowska ◽  
Melanie Lynn Powis ◽  
Nathan Taback ◽  
Christina Diong ◽  
Katherine Enright ◽  
...  
Keyword(s):  
Administrative Data ◽  
Gold Standard ◽  
Validation Cohort ◽  
Systemic Treatment ◽  
Early Stage ◽  
Quality Measures ◽  
Administrative Databases ◽  
Appropriate Use ◽  
Reliability Characteristics

e18269 Background: There is ongoing interest in leveraging administrative data to examine quality but methodological concerns persist. We evaluated the reliability of a previously established panel of administrative data derived quality measures for systemic treatment. Methods: The cohort consisted of women diagnosed with early stage (stage I-III) breast cancer (ESBC) in Ontario, Canada, in 2010. Performance on 11 quality indicators evaluated using deterministically linked healthcare administrative databases has been reported previously. Sensitivity and specificity were examined using the chart as the gold standard. Results: The administrative cohort consisted of 6,795 women with ESBC from which a validation cohort of 705 patients was randomly selected from among patients who underwent cancer surgery at one of five hospitals chosen to balance feasibility and institutional characteristics.Sensitivity and specificity varied by indicator (Table 1). Reliability of some indicators may have been affected by suboptimal chart documentation in instances where care spanned multiple settings or the medical record was fragmented, or where the number of eligible patients for that indicator was low. Conclusions: Administrative data can be used to evaluate quality of systemic cancer therapy but understanding the reliability characteristics of individual indicators is essential to inform their appropriate use and interpretation. [Table: see text]

scholarly journals The quality of open datasets shared by researchers in ecology and evolution is moderately repeatable and slow to change

2021 ◽  
Author(s):  
Dominique Roche ◽  
Ilias Berberi ◽  
Fares Dhane ◽  
Félix lauzon ◽  
Sandrine Soeharjono ◽  
...  
Keyword(s):  
Data Quality ◽  
Data Sharing ◽  
Data Completeness ◽  
Principal Investigators ◽  
Open Datasets ◽  
And Training

We assessed the quality of 362 open datasets shared by 100 principal investigators (PIs) in ecology and evolution to identify predictors of data quality. Datasets generally scored low on completeness and reusability, but these metrics were slightly higher for more recently archived datasets and PIs with less seniority. Journal data sharing policies had no effect on data quality, whereas PI identity explained the largest proportion of the variance in both data completeness (27.8%) and reusability (22.0%), suggesting that a PI’s training and lab culture are key determinants of data quality. Thus, greater incentives and training for individual researchers could help improve data sharing practices.

scholarly journals Design and Estimation of Surveys to Measure Data Quality Aspects of Administrative Data

2012 ◽  
Vol 51 (1) ◽  
pp. 5-16
Author(s):  
James J. Brown ◽  
Oksana Honchar
Keyword(s):  
Data Quality ◽  
Administrative Data ◽  
Data Sources ◽  
Measure Data ◽  
England And Wales ◽  
National Statistics ◽  
Survey Error ◽  
Starting Point ◽  
Quality Aspects

  National Statistics Institutes (NSIs) have been increasingly seeking to replace or enhance traditional survey-baseddata sources with administrative data sources; with the aim to improve overall quality in the absence of a definitive register ofthe population. The Beyond 2011 Census Programme in England and Wales is an example of looking to replace a traditionalcensus with administrative data collected for another purpose by a different organisation, when there is no definitive registeras a starting point. There are also similar projects across NSIs within the area of business surveys looking to useadministrative sources to reduce cost and burden. In this paper we start with considering all aspects of a quality frameworkfor administrative data and then focus on the elements relevant to data quality such as accuracy and coherence. We fit theseconcepts into the framework for total survey error highlighting the components an NSI needs to measure to produce estimatesbased on the administrative data. We then explore the use of both dependent and independent quality surveys to adjust theadministrative data for ‘measurement’ and ‘coverage’ aspects to improve the quality of estimates produced from theadministrative data.

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