scholarly journals Linking a Consortium-Wide Data Quality Assessment Tool with the MIRACUM Metadata Repository

2021 ◽  
Vol 12 (04) ◽  
pp. 826-835
Author(s):  
Lorenz A. Kapsner ◽  
Jonathan M. Mang ◽  
Sebastian Mate ◽  
Susanne A. Seuchter ◽  
Abishaa Vengadeswaran ◽  
...  
Keyword(s):  
Data Integration ◽  
Data Quality ◽  
Quality Assessment ◽  
Research Data ◽  
Specific Information ◽  
Data Element ◽  
Data Repositories ◽  
Data Quality Assessment ◽  
Metadata Repository ◽  
Data Elements

Abstract Background Many research initiatives aim at using data from electronic health records (EHRs) in observational studies. Participating sites of the German Medical Informatics Initiative (MII) established data integration centers to integrate EHR data within research data repositories to support local and federated analyses. To address concerns regarding possible data quality (DQ) issues of hospital routine data compared with data specifically collected for scientific purposes, we have previously presented a data quality assessment (DQA) tool providing a standardized approach to assess DQ of the research data repositories at the MIRACUM consortium's partner sites. Objectives Major limitations of the former approach included manual interpretation of the results and hard coding of analyses, making their expansion to new data elements and databases time-consuming and error prone. We here present an enhanced version of the DQA tool by linking it to common data element definitions stored in a metadata repository (MDR), adopting the harmonized DQA framework from Kahn et al and its application within the MIRACUM consortium. Methods Data quality checks were consequently aligned to a harmonized DQA terminology. Database-specific information were systematically identified and represented in an MDR. Furthermore, a structured representation of logical relations between data elements was developed to model plausibility-statements in the MDR. Results The MIRACUM DQA tool was linked to data element definitions stored in a consortium-wide MDR. Additional databases used within MIRACUM were linked to the DQ checks by extending the respective data elements in the MDR with the required information. The evaluation of DQ checks was automated. An adaptable software implementation is provided with the R package DQAstats. Conclusion The enhancements of the DQA tool facilitate the future integration of new data elements and make the tool scalable to other databases and data models. It has been provided to all ten MIRACUM partners and was successfully deployed and integrated into their respective data integration center infrastructure.

scholarly journals Exploring completeness in clinical data research networks with DQe-c

2017 ◽  
Vol 25 (1) ◽  
pp. 17-24 ◽  
Author(s):  
Hossein Estiri ◽  
Kari A Stephens ◽  
Jeffrey G Klann ◽  
Shawn N Murphy
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Clinical Data ◽  
Data Science ◽  
Ad Hoc ◽  
Assessment Tool ◽  
Data Repository ◽  
Process Data ◽  
Data Repositories ◽  
Data Quality Assessment

Abstract Objective To provide an open source, interoperable, and scalable data quality assessment tool for evaluation and visualization of completeness and conformance in electronic health record (EHR) data repositories. Materials and Methods This article describes the tool’s design and architecture and gives an overview of its outputs using a sample dataset of 200 000 randomly selected patient records with an encounter since January 1, 2010, extracted from the Research Patient Data Registry (RPDR) at Partners HealthCare. All the code and instructions to run the tool and interpret its results are provided in the Supplementary Appendix. Results DQe-c produces a web-based report that summarizes data completeness and conformance in a given EHR data repository through descriptive graphics and tables. Results from running the tool on the sample RPDR data are organized into 4 sections: load and test details, completeness test, data model conformance test, and test of missingness in key clinical indicators. Discussion Open science, interoperability across major clinical informatics platforms, and scalability to large databases are key design considerations for DQe-c. Iterative implementation of the tool across different institutions directed us to improve the scalability and interoperability of the tool and find ways to facilitate local setup. Conclusion EHR data quality assessment has been hampered by implementation of ad hoc processes. The architecture and implementation of DQe-c offer valuable insights for developing reproducible and scalable data science tools to assess, manage, and process data in clinical data repositories.

Towards a Data Quality Framework for Decision Support in a Multidimensional Context

2012 ◽  
Vol 3 (1) ◽  
pp. 17-29
Author(s):  
Daniel Poeppelmann ◽  
Christian Schultewolter
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Decision Process ◽  
Contextual Factors ◽  
Specific Information ◽  
Management Level ◽  
Information Demand ◽  
Data Quality Assessment ◽  
Quality Dimensions ◽  
Data Quality Dimensions

Data quality is defined as fitness for use; meaning it is strongly influenced by contextual factors. None of the proposed methods for data quality assessment considers the management level and decision process phase as important contextual factors. Each combination of different management levels and decision process phases can be described as the overall context in which data quality is to be assessed and leads to a certain information demand. The information demand can in turn be described by a set of features whose values depend on the overall context. A context-sensitive assessment of data quality should therefore be based on data quality dimensions related with certain features characterizing the information demand and their values. This paper proposes a framework for data quality assessment based on the contextual factors management level and decision process phase which allows a decision maker to relate data quality dimensions to values of features of the information demand. Based on this individual assignment of dimensions, the user can access situation specific information regarding data quality.

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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