Developing a Data Quality Standard Primer for Cardiovascular Risk Assessment from Electronic Health Record Data Using the DataGauge Process.

The learning health systems aim to support the needs of patients with chronic diseases, which require methods that account for electronic health recorded (EHR) data limitations. EHR data is often used to calculate cardiovascular risk scores. However, it is unclear whether EHR data presents high enough quality to provide accurate estimates. Still, there is currently no open standard available to assess data quality for such applications. We applied the DataGauge process to develop a data quality standard based on expert clinical, analytical and informatics knowledge by conducting four interviews and one focus group that produced 61 individual data quality requirements. These requirements covered all standard data quality dimensions and uncovered 705 quality issues in EHR data for 456 patients. These requirements will be expanded and further validated in future work. Our work initiates the development of open and explicit data quality standards for specific secondary uses of clinical data.

Building Cancer Diagnosis Text to OncoTree Mapping Pipelines for Clinical Sequencing Data Integration and Curation.

Precision oncology research seeks to derive knowledge from existing data. Current work seeks to integrate clinical and genomic data across cancer centers to enable impactful secondary use. However, integrated data reliability depends on the data curation method used and its systematicity. In practice, data integration and mapping are often done manually even though crucial data such as oncological diagnoses (DX) show varying accuracy and specificity levels. We hypothesized that mapping of text-form cancer DX to a standardized terminology (OncoTree) could be automated using existing methods (e.g. natural language processing (NLP) modules and application programming interfaces [APIs]). We found that our best-performing pipeline prototype was effective but limited by API development limitations (accurately mapped 96.2% of textual DX dataset to NCI Thesaurus (NCIt), 44.2% through NCIt to OncoTree). These results suggest the pipeline model could be viable to automate data curation. Such techniques may become increasingly more reliable with further development.