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IHI ID 03 EQuIP, an evidence-based quality improvement process: improving the speed to insight
  1. Jose Benuzillo,
  2. Lucy Savitz
  1. Kaiser Permanente, USA


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:

  1. network leakage;

  2. avoidable hospital days; and

  3. 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.

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