Health Ontology Mapper A project initiated within the CTSA (Clinical Translation Science Awards) program Goal: create a semantic interoperability layer for the exchange of health data to facilitate biomedical informatics research. 1

General Purpose Instance Mapper The Health Ontology Mapper is the first open source general purpose and automated Instance Mapper. HOM translates the physical encoding of health information into standard data encodings to facilitate the secondary use of that same information within different contexts. 2

Scope It is important to note that the Ontology Mapper does Not focus on Association Mapping. Association maps provide the relationships that exist between different data standards. The leading Association Mapping software is lexBIG and is used on the NCBO Bioportal. Ontology Mapper Does provide Instance Mapping and so specifically concentrates on the translation of the physical encoding of health information data. As such HOM is extremely useful for the translation of information collected from commercial software environments into important data standards used within the health industry (such as LOINC, ICD-9, OCRe, SNOMED CT, HOMERUN etc) 3

Use Cases Ontology Mapper fulfills several critical use cases within medical informatics. Those include the building of an enterprise data warehouse for translational research (usually referred to as an IDR or Integrated Data Repository, such as i2b2.org) Ontology Mapper also facilitates the inter-institutional exchange of data while maintaining a common definition of what that data means and how it is encoded. (commonly referred to as semantic interoperability.) Two very active implementation projects involving HOM include the exchange of medical data over both the Harvard SHRINE protocol and over the OSU caGRID network originally developed as part of caBIG. 4

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Data Discovery A description of the User Interface of Ontology Mapper can be useful for understanding its purpose and function. A typical user session would begin by viewing the Data Discovery interface. Data Discovery provides a description of what locally encoded information is available. An investigator is then allowed to request to specific sources of information by placing checkmarks next to them. 6

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Query Dashboard The HOM system allows investigators to view previous requests for information from the data warehouse or grid. Previous queries for information can be reloaded and used as the basis for future queries. 8

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Instance Mapping The Mapping Tab allows the investigator to view how native data is encoded. Often that locally encoded information will not be encoded in any recognized data standard. The user can then request that the information be mapped into any ISO based data model. Standardized formats are stored within any Data Standards Repository (DSR) such as the caDSR. 10

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Compliance Ontology Mapper also tracks the compliance with data privacy laws such as HIPAA. The Data Request tab is used to track the contact information for investigators and the delivery of data extracts for further analysis. 12

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One Tool Many Purposes The Ontology Mapper has been a long and exciting project that is now approaching a 2 year duration. We are now in the process of launching this open source project into general release and open to exciting new opportunities for it’s application within the health industry in general and within health research in particular using grid technology. 14

Recombinant Data Corp. April Entered into agreement with the Trustees of the University of California to become the exclusive service provider for Ontomapper Created a hive cell that integrates directly with the i2b2 workbench 15

There are now 2 grids: HSDB, and STIRs (Radiology) are on caGRID DBRD and COHRI are on SHRINE and QSN (now called HOMERUN) is on BOTH 16