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Background Despite the recognition that data can create value for healthcare organizations, only a few have adopted rigorous analytic approaches to support their data exploration efforts. With new sources and increasing amounts of data available to guide quality improvement work, having an evidence-based approach for directing data analytics is essential. Objectives To propose an evidence-guided systematic approach for exploring data to identify QI opportunities. Methods We developed a linear framework that begins with monitoring the literature in target clinical areas of importance. This review of existing knowledge by a multidisciplinary team provides parameters that guide data exploration and facilitates the selection of benchmarks and potential balance measures. Then, a robust dataset is built by combining relevant data sources. Next, data are analyzed using a combination of traditional statistical and quality improvement techniques. Lastly, key points are summarized and presented to stake holders as specific improvement opportunities in the context of business value; listing actionable targets with estimated impact. Results The proposed methodology was tested on one of Kaisers target clinical areas of importance, interventional cardiology. Three potential improvement opportunities were identified: network leakage; avoidable hospital days; and preventable 30 day hospital readmission. Preliminary estimates suggested that our data could lead to approximately 1 million dollars in savings and up to 250 avoided hospital days while improving the quality and safety of care to our members. Conclusions Systematic use of a framework for data exploration may create operational and strategic business value by increasing the speed at which data are transformed into actionable knowledge.Abstract IHI ID 03 Figure 1Abstract IHI ID 03 Figure 2

IHI ID 03 EQuIP, an evidence-based quality improvement process: improving the speed to insight