Such trends include the increasing adoption and diversity of EHRs to capture longitudinal patient information rapid development and early adoption of genomics, imaging, and other complex data types the progressive organization of multidisciplinary teams focusing on systems biology research and the need for integration and exchange of many different types and complexities of data. 6 Trends in translational research indicate a need for data discovery platforms that can stage and disseminate data in a readily accessible form to researchers focusing on disease. To demonstrate its effectiveness, Harvest was used to develop and deploy intuitive data discovery applications for two distinctive biomedical domains: pediatric cardiology diagnostic modality and procedure data generated at The Children's Hospital of Philadelphia (CHOP), and infectious disease data published by the OpenMRS open-source electronic health record (EHR) project.Īdoption of EHR systems by academic medical centers has created significant potential for the re-use of clinical data for research. Our primary development objectives were to (1) provide for researchers with limited informatics ability a toolkit to generate meaningful views of raw data according to their domain expertise and their specific interests (2) dynamically query key aspects of a dataset based on the inherent characteristics of individual data attributes (3) combine single attribute queries into multiattribute set operation queries and (4) provide an actionable endpoint by exporting immediately available raw data in an analysis-ready format. 5 As a result, researchers without access to sophisticated informatics expertise are increasingly challenged with efficiently managing, exploring, and understanding the information at their disposal.Īccordingly, we developed Harvest, a new biomedical data application framework.
Moreover, existing query and reporting tools tend to be general-purpose instruments with user interfaces designed to support expert analysts working in a variety of situations. 3 4 Unlike purely transactional data, such as those typically derived from business operations, these data are not readily summed or averaged, limiting the utility of traditional business intelligence tools in this context. 2 Research data complexity is amplified by the high volume of data points generated by modern molecular and imaging platforms. 1 Datasets useful to biomedical research are typically complex, highly dimensional, and temporal, often with significant variation in granularity, sparsity, and representation across data dimensions. All resources, including the OpenMRS demonstration, can be found at īiomedical researchers are often challenged with navigating the large volumes of data available from medical and research information systems.
#HARVEST APP APPLICATION CODE#
Harvest's architecture and public open-source code offer a set of rapid application development tools to build data discovery applications for domain-specific biomedical data repositories. We evaluated Harvest with two cases: clinical data from pediatric cardiology and demonstration data from the OpenMRS project.
Harvest incorporates visualizations of highly dimensional data in a web-based interface that promotes rapid exploration and export of any type of biomedical information, without exposing researchers to underlying data models.
#HARVEST APP APPLICATION SOFTWARE#
We have addressed this need by developing Harvest, an open-source framework of modular components, and using it for the rapid development and deployment of custom data discovery software applications. Data discovery in this context is limited by a lack of query systems that efficiently show relationships between individual variables, but without the need to navigate underlying data models.
Biomedical researchers share a common challenge of making complex data understandable and accessible as they seek inherent relationships between attributes in disparate data types.
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