Luke's University Health Network uses data as a strategic part of its operations across all of its sites and with its approach to self-service business intelligence and analytics. Until recently, the Bethlehem, Pennsylvania-based St. Luke's, a nine-hospital system with more than outpatient sites, had varied data sources that made it difficult to obtain a single view of key information needed to support clinical, operational and financial analysis. But this past year, the health system contracted with Information Builders, an analytics and master patient index vendor, to develop more than 60 applications — dubbed InfoApps — that help its stakeholders monitor financial, clinical and operational activities through self-service and guided analytics built on integrated and mastered data domains. Luke's University Health Network. For more advanced users, St.
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A Holistic View of Data Warehousing in Education | IEEE Journals & Magazine | IEEE Xplore
When I work with healthcare organizations to teach them how to unlock the value of their data , I hear a lot of talk about how important it is to have a tool like a clinical data repository. But in my experience, this belief is limiting: a clinical data repository is just that—a repository. Instead, what health systems need is a flexible, late-binding enterprise data warehouse EDW. With its unique ability to flexibly tie disparate data sources from across the organization into one source of truth, health systems will realize a significant return of investment ROI from their newfound ability to quickly and easily pull and analyze data for every service in the organization. A clinical data repository consolidates data from various clinical sources, such as an EMR or a lab system, to provide a full picture of the care a patient has received.
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Comprehensive survey on data warehousing research
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A Data Warehouse DW stores corporate information and data from operational systems and a wide range of other data resources. Data Warehouses are designed to support the decision-making process through data collection, consolidation, analytics, and research. The architecture for Data Warehouses was developed in the s to assist in transforming data from operational systems to decision-making support systems. In a Data Warehouse , data from many different sources is brought to a single location and then translated into a format the Data Warehouse can process and store. The boss may ask about the latest cost-reduction measures, and getting answers will require an analysis of all of the previously mentioned data.