Data Quality Assessment

2014 ◽  
Vol 4 (1) ◽  
pp. 24-36 ◽  
Author(s):  
Juliusz L. Kulikowski
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
State Of The Art ◽  
Quality Measures ◽  
Future Trends ◽  
Quality Maintenance ◽  
Historical View ◽  
Data Quality Assessment ◽  
Level Data ◽  
Single Data

A state-of-the-art in the domain of data quality assessment and maintaining in modern information systems is presented in the paper. A short historical view on the development of this problem is given. Particular attention is paid to the development of the idea of multi-aspect data quality assessment. The problem of extension of single data quality assessment on higher-level data structures is considered. The methods of multi-aspect data quality measures ordering for comparison is analyzed and its solution based on the concept of semi-ordering in linear vector (Kantorovitsh) space is proposed. Remarks on organizational and technological tools for data quality maintenance in organizations are given. Expected future trends in the development of data quality assessment and maintenance methods are suggested.

scholarly journals Application of an Ontology for Characterizing Data Quality for a Secondary Use of EHR Data

2016 ◽  
Vol 07 (01) ◽  
pp. 69-88 ◽  
Author(s):  
Stuart Speedie ◽  
Gyorgy Simon ◽  
Vipin Kumar ◽  
Bonnie Westra ◽  
Steven Johnson
Keyword(s):  
Electronic Health Record ◽  
Data Quality ◽  
Quality Assessment ◽  
Quality Measures ◽  
Assessment Process ◽  
Health Record ◽  
Secondary Use ◽  
Data Quality Assessment ◽  
Data Quality Metrics ◽  
And Task

SummaryThe goal of this study is to apply an ontology based assessment process to electronic health record (EHR) data and determine its usefulness in characterizing data quality for calculating an example eMeasure (CMS178).The process uses a data quality ontology that references separate data quality, domain and task ontologies to compute measures based on proportions of constraints that are satisfied. These quantities indicate how well the data conforms to the domain and how well it fits the task.The process was performed on a de-identified 200,000 encounter sample from a hospital EHR. CodingConsistency was poor (44%) but DomainConsistency (97%) and TaskRelevance (95%) were very good. Improvements in the data quality Measures correlated with improvements in the eMeasure.This approach can encourage the development of new detailed Domain ontologies that can be reused for data quality purposes across different organizations’ EHR data. Automating the data quality assessment process using this method can enable sharing of data quality metrics that may aid in making research results that use EHR data more transparent and reproducible.

Applying Domain Knowledge for Data Quality Assessment in Dermatology

2017 ◽  
pp. 147-156
Author(s):  
Nemanja Igić ◽  
Branko Terzić ◽  
Milan Matić ◽  
Vladimir Ivančević ◽  
Ivan Luković
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Domain Knowledge ◽  
Data Quality Assessment

scholarly journals Evaluation of a staff training programme to reimplement a comprehensive health assessment

2018 ◽  
Vol 7 (4) ◽  
pp. e000353 ◽  
Author(s):  
Luke A Turcotte ◽  
Jake Tran ◽  
Joshua Moralejo ◽  
Nancy Curtin-Telegdi ◽  
Leslie Eckel ◽  
...  
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Health Information ◽  
Health Assessment ◽  
Training Programme ◽  
Care Planning ◽  
Assessment Framework ◽  
High Quality ◽  
Data Quality Assessment ◽  
Comprehensive Health

BackgroundHealth information systems with applications in patient care planning and decision support depend on high-quality data. A postacute care hospital in Ontario, Canada, conducted data quality assessment and focus group interviews to guide the development of a cross-disciplinary training programme to reimplement the Resident Assessment Instrument–Minimum Data Set (RAI-MDS) 2.0 comprehensive health assessment into the hospital’s clinical workflows.MethodsA hospital-level data quality assessment framework based on time series comparisons against an aggregate of Ontario postacute care hospitals was used to identify areas of concern. Focus groups were used to evaluate assessment practices and the use of health information in care planning and clinical decision support. The data quality assessment and focus groups were repeated to evaluate the effectiveness of the training programme.ResultsInitial data quality assessment and focus group indicated that knowledge, practice and cultural barriers prevented both the collection and use of high-quality clinical data. Following the implementation of the training, there was an improvement in both data quality and the culture surrounding the RAI-MDS 2.0 assessment.ConclusionsIt is important for facilities to evaluate the quality of their health information to ensure that it is suitable for decision-making purposes. This study demonstrates the use of a data quality assessment framework that can be applied for quality improvement planning.

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