scholarly journals WaPOR V2 quality assessment – Technical Report on the Data Quality of the WaPOR FAO Database version 2

2020 ◽  
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Technical Report

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 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.

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.

Data quality assessment in hydrological information systems

2015 ◽  
Vol 17 (4) ◽  
pp. 640-661
Author(s):  
Li Chao ◽  
Zhou Hui ◽  
Zhou Xiaofeng
Keyword(s):  
Supply Chain ◽  
Data Quality ◽  
Quality Assessment ◽  
Assessment System ◽  
Assessment Model ◽  
Quality Of Data ◽  
Cognitive Differences ◽  
Hydrological Data ◽  
Low Performance

The hydrological data fed to hydrological decision support systems might be untimely, incomplete, inconsistent or illogical due to network congestion, low performance of servers, instrument failures, human errors, etc. It is imperative to assess, monitor and even control the quality of hydrological data residing in or acquired from each link of a hydrological data supply chain. However, the traditional quality management of hydrological data has focused mainly on intrinsic quality problems, such as outlier detection, nullity interpolation, consistency, completeness, etc., and could not be used to assess the quality of application – that is, consumed data in the form of data supply chain and with a granularity of tasks. To achieve these objectives, we first present a methodology to derive quality dimensions from hydrological information system by questionnaire and show the cognitive differences in quality dimension importance, then analyze the correlations between the tasks, classify them into five categories and construct the quality assessment model with time limits in the data supply chain. Exploratory experiments suggest the assessment system can provide data quality (DQ) indicators to DQ assessors, and enable authorized consumers to monitor and even control the quality of data used in an application with a granularity of tasks.

scholarly journals Importance of Continued Data Quality Assessment of Syndromic Production Data

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Sophia Crossen
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Production Data ◽  
Quality Of Data ◽  
Data Set ◽  
Quality Degradation ◽  
Periodic Data ◽  
Data Quality Assessment

ObjectiveTo explore the quality of data submitted once a facility is movedinto an ongoing submission status and address the importance ofcontinuing data quality assessments.IntroductionOnce a facility meets data quality standards and is approved forproduction, an assumption is made that the quality of data receivedremains at the same level. When looking at production data qualityreports from various states generated using a SAS data qualityprogram, a need for production data quality assessment was identified.By implementing a periodic data quality update on all productionfacilities, data quality has improved for production data as a whole andfor individual facility data. Through this activity several root causesof data quality degradation have been identified, allowing processesto be implemented in order to mitigate impact on data quality.MethodsMany jurisdictions work with facilities during the onboardingprocess to improve data quality. Once a certain level of data qualityis achieved, the facility is moved into production. At this point thejurisdiction generally assumes that the quality of the data beingsubmitted will remain fairly constant. To check this assumption inKansas, a SAS Production Report program was developed specificallyto look at production data quality.A legacy data set is downloaded from BioSense production serversby Earliest Date in order to capture all records for visits which occurredwithin a specified time frame. This data set is then run through a SASdata quality program which checks specific fields for completenessand validity and prints a report on counts and percentages of null andinvalid values, outdated records, and timeliness of record submission,as well as examples of records from visits containing these errors.A report is created for the state as a whole, each facility, EHR vendor,and HIE sending data to the production servers, with examplesprovided only by facility. The facility, vendor, and HIE reportsinclude state percentages of errors for comparison.The Production Report was initially run on Kansas data for thefirst quarter of 2016 followed by consultations with facilities on thefindings. Monthly checks were made of data quality before and afterfacilities implemented changes. An examination of Kansas’ resultsshowed a marked decrease in data quality for many facilities. Everyfacility had at least one area in need of improvement.The data quality reports and examples were sent to every facilitysending production data during the first quarter attached to an emailrequesting a 30-60 minute call with each to go over the report. Thiscall was deemed crucial to the process since it had been over a year,and in a few cases over two years, since some of the facilities hadlooked at data quality and would need a review of the findings andall requirements, new and old. Ultimately, over half of all productionfacilities scheduled a follow-up call.While some facilities expressed some degree of trepidation, mostfacilities were open to revisiting data quality and to making requestedimprovements. Reasons for data quality degradation included updatesto EHR products, change of EHR product, work flow issues, engineupdates, new requirements, and personnel turnover.A request was made of other jurisdictions (including Arizona,Nevada, and Illinois) to look at their production data using the sameprogram and compare quality. Data was pulled for at least one weekof July 2016 by Earliest Date.ResultsMonthly reports have been run on Kansas Production data bothbefore and after the consultation meetings which indicate a markedimprovement in both completeness of required fields and validityof values in those fields. Data for these monthly reports was againselected by Earliest Date.ConclusionsIn order to ensure production data continues to be of value forsyndromic surveillance purposes, periodic data quality assessmentsshould continue after a facility reaches ongoing submission status.Alterations in process include a review of production data at leasttwice per year with a follow up data review one month later to confirmadjustments have been correctly implemented.

scholarly journals Quality assessment for register-based statistics - Results for the Austrian census 2011

2016 ◽  
Vol 45 (2) ◽  
pp. 3-14 ◽  
Author(s):  
Eva-Maria Asamer ◽  
Franz Astleithner ◽  
Predrag Cetkovic ◽  
Stefan Humer ◽  
Manuela Lenk ◽  
...  
Keyword(s):  
Data Processing ◽  
Data Quality ◽  
Quality Assessment ◽  
Quality Indicator ◽  
Quality Evaluation ◽  
External Source ◽  
Quality Framework ◽  
New Challenges ◽  
Available Information

In 2011, Statistics Austria carried out the first register-based census. The use of administrative data for statistical purposes is accompanied by various advantages like a reduced burden for the respondents and less costs for the NSI. However, new challenges, like the quality assessment of this kind of data, arise. Therefore, Statistics Austria developed a comprehensive standardized framework for the evaluation of the data quality for registerbased statistics.In this paper, we present the principle of the quality framework and detailed results from the quality evaluation of the 2011 Austrian census. For each attribute in the census a quality measure is derived from four hyperdimensions. The first three hyperdimensions focus on the documentation of data, the usability of the records and the comparison of data to an external source. The fourth hyperdimension assesses the quality of the imputations. In the framework all the available information on each attribute can be combined to form one final quality indicator. This procedure allows to track changes in quality during data processing and to compare the quality of different census generations.

scholarly journals Perspectives on Citizen Science Data Quality

2021 ◽  
Vol 3 ◽  
Author(s):  
Robert R. Downs ◽  
Hampapuram K. Ramapriyan ◽  
Ge Peng ◽  
Yaxing Wei
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Quality Assessment ◽  
Citizen Science ◽  
Data Reuse ◽  
Quality Information ◽  
Science Data ◽  
Conducting Research

Information about data quality helps potential data users to determine whether and how data can be used and enables the analysis and interpretation of such data. Providing data quality information improves opportunities for data reuse by increasing the trustworthiness of the data. Recognizing the need for improving the quality of citizen science data, we describe quality assessment and quality control (QA/QC) issues for these data and offer perspectives on aspects of improving or ensuring citizen science data quality and for conducting research on related issues.

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