OSF HealthCare has committed that 75 percent of its primary care patient will be part of a value-based program by 2020. The organization’s leaders knew that success depended on how well they managed their data and decided to build a data warehouse in-house. They recognized that beneficiary claims data was essential to understanding their population better. To get that claims data, however, was no easy task. This required patient matching through master data management and getting buy-in from leaders and physicians throughout the health system. Then, they prioritize where to start efforts using the 80/20 rule and using that as a guide, loaded the claims data.
Learn more about Roopa Foulger
Roopa Foulger has 17 years’ experience in Enterprise Information Management and Business Intelligence. She is currently the Executive Director - Healthcare Analytics at OSF HealthCare System and has been with the organization for 5 years. Prior to joining OSF she was leading Enterprise Data Warehouse projects for Caterpillar in consulting capacity. She worked in GlaxoSmithkline, as Senior Architect and Project Manager successfully deploying Customer/Market focused analytics, Product forecasting, and Performance Scorecards. Beyond Health sciences market she has created business intelligence solutions for Telecommunications, Banking and Insurance sectors. Roopa has a degree in Bachelor OF Science Electrical Engineering from Bharathiar Universtity, India.
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Healthcare analytics are essential for organizations to thrive in the new healthcare environment. Using analytics, systems can evaluate efficiency, effectiveness, and find improvement opportunities. There are two principal approaches: outsourcing the analytics function to benchmarking companies and providers of software-as-a-service; and doing analytics in-house with a system’s own data warehouse. The pros of outsourcing include gaining benchmarking access to how health system peers are performing. The cons to outsourcing include focusing too much high-level outcomes with no insight in how to effect change. The pros of in-house analytics include having quick access to fine-grained details of the data and being able to include clinicians in the implementation and development of the analytics process. A con is that in-house analytics can require significant resources – an investment in the right personnel and right technology.