The Value of Value Sets

A common definition of value set will be provided and fully characterized relative to its proposed uses. We will describe, compare, and contrast several approaches to specifying and referencing value sets in a stable manner over time. The term “value set”, although ubiquitous within biomedical informatics has no common definition and has yet to be fully described in a formal manner. It is essential for the design and launch of new ontologies, biomedical informatics applications and data sharing environments that a common and well-­‐ understood definition of “value set” is provided. It is also essential that options and trade-­‐offs be understood for what type of technology is appropriate for the implementation and usage of particular types of value set for particular use cases

The Human Studies Database Project: Federating Human Studies Design Data Using the Ontology of Clinical Research

Human studies, encompassing interventional and observational studies, are the most important source of evidence for advancing our understanding of health, disease, and treatment options. To promote discovery, the design and results of these studies should be made machine-readable for large-scale data mining, synthesis, and re-analysis. The Human Studies Database Project aims to define and implement an informatics infrastructure for institutions to share the design of their human studies. We have developed the Ontology of Clinical Research (OCRe) to model study features such as design type, interventions, and outcomes to support scientific query and analysis. We are using OCRe as the reference semantics for federated data sharing of human studies over caGrid, and are piloting this implementation with several Clinical and Translational Science Award (CTSA) institutions

Ontology Mapping and Data Discovery for the Translational Investigator

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

The Health Ontology Mapper (HOM) Method Semantic Interoperability at Scale

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

Health Ontology Mapper (HOM): Semantic Interoperability at Scale

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

Lab Norm: Automated Clinical Lab Data Normalization

Within the CTSA (Clinical Translational Sciences Awards) program academic medical centers are tasked with the storage of clinical laboratory data within an Integrated Data Repository (IDR) and the subsequent exposure of that data over grid computing environments for hypothesis generation and cohort selection. Lab data that is collected from multiple machines over long periods of time from many labs and across multiple institutions requires normalization before data sets can be aggregated and compared. However, lab data normalization is difficult when published reference intervals are not always reliable and when the lab data collected is not always normally distributed. This paper sets forth a proposed solution to the challenge of generating derived aggregated normalized views from large, distributed data sets of clinical lab data intended for re-use within clinical translational research

Implementation of a deidentified federated data network for population-based cohort discovery.

ObjectiveThe Cross-Institutional Clinical Translational Research project explored a federated query tool and looked at how this tool can facilitate clinical trial cohort discovery by managing access to aggregate patient data located within unaffiliated academic medical centers.MethodsThe project adapted software from the Informatics for Integrating Biology and the Bedside (i2b2) program to connect three Clinical Translational Research Award sites: University of Washington, Seattle, University of California, Davis, and University of California, San Francisco. The project developed an iterative spiral software development model to support the implementation and coordination of this multisite data resource.ResultsBy standardizing technical infrastructures, policies, and semantics, the project enabled federated querying of deidentified clinical datasets stored in separate institutional environments and identified barriers to engaging users for measuring utility.DiscussionThe authors discuss the iterative development and evaluation phases of the project and highlight the challenges identified and the lessons learned.ConclusionThe common system architecture and translational processes provide high-level (aggregate) deidentified access to a large patient population (>5 million patients), and represent a novel and extensible resource. Enhancing the network for more focused disease areas will require research-driven partnerships represented across all partner sites