scholarly journals Reporting Data Quality Assessment Results: Identifying Individual and Organizational Barriers and Solutions

2017 ◽  
Vol 5 (1) ◽  
pp. 16 ◽  
Author(s):  
Tiffany J. Callahan ◽  
Juliana G. Barnard ◽  
Laura J. Helmkamp ◽  
Julie A. Maertens ◽  
Michael G. Kahn
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Organizational Barriers ◽  
Data Quality Assessment ◽  
Reporting Data ◽  
Assessment Results

scholarly journals An RDF Data Set Quality Assessment Mechanism for Decentralized Systems

2020 ◽  
Vol 2 (4) ◽  
pp. 529-553
Author(s):  
Li Huang ◽  
Zhenzhen Liu ◽  
Fangfang Xu ◽  
Jinguang Gu
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Linked Data ◽  
Cost Effective ◽  
Assessment Model ◽  
Maintenance Cost ◽  
Data Set ◽  
Data Quality Assessment ◽  
Rdf Data ◽  
Assessment Results

With the rapid growth of the linked data on the Web, the quality assessment of the RDF data set becomes particularly important, especially for the quality and accessibility of the linked data. In most cases, RDF data sets are shared online, leading to a high maintenance cost for the quality assessment. This also potentially pollutes Internet data. Recently blockchain technology has shown the potential in many applications. Using the blockchain storage quality assessment results can reduce the centralization of the authority, and the quality assessment results have characteristics such as non-tampering. To this end, we propose an RDF data quality assessment model in a decentralized environment, pointing out a new dimension of RDF data quality. We use the blockchain to record the data quality assessment results and design a detailed update strategy for the quality assessment results. We have implemented a system DCQA to test and verify the feasibility of the quality assessment model. The proposed method can provide users with better cost-effective results when knowledge is independently protected.

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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