Background: Taiwan has 32 biobanks under Government’ governance. The Ministry of Health and Welfare have established a National Biobank Consortium of Taiwan to unify the specimen quality and the medical record database. The total recruited participants exceed 350,000. The National Health Research Institutes in Taiwan hold the responsibility of establish a common data model for aggregating data elements from electronic health records (EHRs) of institutes through direct feeds. The goals are to assemble a set of common oncology data elements and to facilitate cancer data interoperability for patient care and research across institutes of Biobank Consortium. Methods: We first conduct a thorough review of available EHR data elements for patient characteristics, diagnosis/staging, treatments, laboratory results, vital signs and outcomes. The data dictionary was organized based on HL7 FHIR and also included data elements from Taiwan Cancer Registry (TCR) and National Health Insurance (NHI) Program, which the common definition has already been established and implemented for years. Data elements suggested by ASCO CancerLinQ and minimal Common Oncology Data Elements (mCODE) are also referenced during planning. The final common model was then reviewed by a panel of experts consisting oncologists as well as data science specialists. Results: There are finally 9 data tables with 281 data elements, in which 248 of them are from the routinely uploaded data elements to government agencies (TCR & NHI) and 33 elements are collected with partial common definition among institutes. There are 164 data elements which are to be collected one observation per case, while 117 elements will be accumulated periodically. Conclusions: A comprehensive understanding of genetics, phenotypes, disease variation as well as treatment responses is crucial to fulfill the needs of real-world studies, which potentially would lead to personalized treatment and drug development. At the first stage of this project, we aim to accumulate available EHR structured data elements and to maintain sufficient cancer data quality. Consequently, the database can provide real-world evidence to promote evidence-based & data-driven cancer care.
Date:
2020-05
Relation:
Journal of Clinical Oncology. 2020 May;38(15, Suppl.):Meeting Abstract e19283.
