Structured Data Capture for Oncology.

Lack of interoperability is one of the greatest challenges facing healthcare informatics. Recent interoperability efforts have focused primarily on data transmission and generally ignore data capture standardization. Structured Data Capture (SDC) is an open-source technical framework that enables the capture and exchange of standardized and structured data in interoperable data entry forms (DEFs) at the point of care. Some of SDC's primary use cases concern complex oncology data such as anatomic pathology, biomarkers, and clinical oncology data collection and reporting. Its interoperability goals are the preservation of semantic, contextual, and structural integrity of the captured data throughout the data's lifespan. SDC documents are written in eXtensible Markup Language (XML) and are therefore computer readable, yet technology agnostic-SDC can be implemented by any EHR vendor or registry. Any SDC-capable system can render an SDC XML file into a DEF, receive and parse an SDC transmission, and regenerate the original SDC form as a DEF or synoptic report with the response data intact. SDC is therefore able to facilitate interoperable data capture and exchange for patient care, clinical trials, cancer surveillance and public health needs, clinical research, and computable care guidelines. The usability of SDC-captured oncology data is enhanced when the SDC data elements are mapped to standard terminologies. For example, an SDC map to Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) enables aggregation of SDC data with other related data sets and permits advanced queries and groupings on the basis of SNOMED CT concept attributes and description logic. SDC supports terminology maps using separate map files or as terminology codes embedded in an SDC document.

Employing complex polyhierarchical ontologies and promoting interoperability of i2b2 data systems.

I2b2 is in widespread use for managing research data warehouses. It employs reference ontologies as a record index and supports searching for aggregate cases using a pattern match operator on ASCII strings representing the node traversal from root to concept(PATHs). This creates complexities in dissemination and deployment for large polyhierarchical ontologies such as SNOMED CT. We hypothesized that an alternative approach employing transitive closure tables (TC) could lead to more accurate, efficient and interoperable search tools for i2b2. We evaluated search speed, accuracy and interoperability of queries employing each approach. We found both TC-based and PATH-based queries to produce accurate results. However, we observed that TC-based queries involving concepts included in large numbers of paths ran substantially faster than PATH-based queries for the same concept. Oracle query plan resource estimates differed by one to three orders of magnitude for these queries. We conclude that a simplification of dissemination tools for SNOMED CT and revision in the metadata build for i2b2 can effectively employ SNOMED CT with increased efficiency and comparable accuracy. Use of transitive closure tables in metadata can promote network query interoperability.