QUANTIS: Data quality assessment tool by clustering analysis

2019 ◽  
Vol 51 (11) ◽  
pp. 872-885
Author(s):  
Steffen H. Symoens ◽  
Syam Ukkandath Aravindakshan ◽  
Florence H. Vermeire ◽  
Kevin De Ras ◽  
Marko R. Djokic ◽  
...  
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Clustering Analysis ◽  
Assessment Tool ◽  
Quality Assessment Tool ◽  
Data Quality Assessment

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.

scholarly journals Clinical Research Informatics: Contributions from 2018

2019 ◽  
Vol 28 (01) ◽  
pp. 203-205 ◽  
Author(s):  
Christel Daniel ◽  
Dipak Kalra ◽  
Keyword(s):  
Clinical Research ◽  
Data Quality ◽  
Quality Assessment ◽  
Clinical Data ◽  
Hospital Admissions ◽  
Assessment Tool ◽  
Editorial Team ◽  
Free Text ◽  
Data Quality Assessment ◽  
Clinical Research Informatics

Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2018. Method: A bibliographic search using a combination of MeSH descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting of the editorial team was organized to conclude on the selection of best papers. Results: Among the 1,469 retrieved papers published in 2018 in the various areas of CRI, the full review process selected four best papers. The first best paper describes a simple algorithm detecting co-morbidities in Electronic Healthcare Records (EHRs) using a clinical data warehouse and a knowledge base. The authors of the second best paper present a federated algorithm for predicting heart failure hospital admissions based on patients' medical history described in their distributed EHRs. The third best paper reports the evaluation of an open source, interoperable, and scalable data quality assessment tool measuring completeness of data items, which can be run on different architectures (EHRs and Clinical Data Warehouses (CDWs) based on PCORnet or OMOP data models). The fourth best paper reports a data quality program conducted across 37 hospitals addressing data quality Issues through the whole data life cycle from patient to researcher. Conclusions: Research efforts in the CRI field currently focus on consolidating promises of early Distributed Research Networks aimed at maximizing the potential of large-scale, harmonized data from diverse, quickly developing digital sources. Data quality assessment methods and tools as well as privacy-enhancing techniques are major concerns. It is also notable that, following examples in the US and Asia, ambitious regional or national plans in Europe are launched that aim at developing big data and new artificial intelligence technologies to contribute to the understanding of health and diseases in whole populations and whole health systems, and returning actionable feedback loops to improve existing models of research and care. The use of “real-world" data is continuously increasing but the ultimate role of this data in clinical research remains to be determined.

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