Evaluation of data quality of multisite electronic health record data for secondary analysis

2015 ◽  
Author(s):  
Alicia L. Nobles ◽  
Ketki Vilankar ◽  
Hao Wu ◽  
Laura E. Barnes
Keyword(s):  
Electronic Health Record ◽  
Data Quality ◽  
Secondary Analysis ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health ◽  
Evaluation Of Data

scholarly journals Desiderata for the development of next-generation electronic health record phenotype libraries

GigaScience ◽  
2021 ◽  
Vol 10 (9) ◽  
Author(s):  
Martin Chapman ◽  
Shahzad Mumtaz ◽  
Luke V Rasmussen ◽  
Andreas Karwath ◽  
Georgios V Gkoutos ◽  
...  
Keyword(s):  
Best Practices ◽  
Electronic Health Record ◽  
Health Record ◽  
Next Generation ◽  
Electronic Health Record Data ◽  
High Quality ◽  
Record Data ◽  
Electronic Health ◽  
Further Development

Abstract Background High-quality phenotype definitions are desirable to enable the extraction of patient cohorts from large electronic health record repositories and are characterized by properties such as portability, reproducibility, and validity. Phenotype libraries, where definitions are stored, have the potential to contribute significantly to the quality of the definitions they host. In this work, we present a set of desiderata for the design of a next-generation phenotype library that is able to ensure the quality of hosted definitions by combining the functionality currently offered by disparate tooling. Methods A group of researchers examined work to date on phenotype models, implementation, and validation, as well as contemporary phenotype libraries developed as a part of their own phenomics communities. Existing phenotype frameworks were also examined. This work was translated and refined by all the authors into a set of best practices. Results We present 14 library desiderata that promote high-quality phenotype definitions, in the areas of modelling, logging, validation, and sharing and warehousing. Conclusions There are a number of choices to be made when constructing phenotype libraries. Our considerations distil the best practices in the field and include pointers towards their further development to support portable, reproducible, and clinically valid phenotype design. The provision of high-quality phenotype definitions enables electronic health record data to be more effectively used in medical domains.

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