UCSF

The Health Ontology Mapper (HOM) method is a proposed solution to the semantic gap problem. The HOM Method provides the following functionality to enable the scalable deployment of informatics systems involving data from multiple health systems. The HOM method allows a relatively small population of biomedical ontology experts to describe the interpretation and analysis of biomedical information collected at thousands of hospitals via a cloud based terminology server. As such the HOM Method is focused on the scalability of the human talent required for successful informatics projects. The HOM promotes a means of converting UML based medical data into OWL format via a cloud-based method of controlling the data loading process. HOM subscribes to a means of converting data into a HIPAA Limited Data Set format to lower the risk associated with developing large virtual data repositories. HOM also provides a means of allowing access to medical data over grid computing environments by translating all information via a centralized web-based terminology server technology.

An integrated data repository (IDR) containing aggregations of clinical, biomedical, economic, administrative, and public health data is a key component of research infrastructure, quality improvement and decision support. But most available medical data is encoded using standard data warehouse architecture that employs arbitrary data encoding standards, making queries across disparate repositories difficult. In response to these shortcomings the Health Ontology Mapper (HOM) translates terminologies into formal data encoding standards without altering the underlying source data. The HOM method promotes inter-institutional data sharing and research collaboration, and will ultimately lower the barrier to developing and using an IDR.