Improvement networks, by design, are focused on developing and testing changes based upon theories of improvement (and providing for the spread of those changes that demonstrate merit and potential). These actions are ideally based upon practical evidence brought forth from rigorous disciplined inquiry. To succeed, networks must learn quickly by studying their own practices, continuously adapting to changing circumstances within their organizations and in the broader environment, and incorporating this learning into their ongoing work.

This paper offers an integrated perspective on how evidence can be marshaled by network leaders and their analytic partners to inform improvement networks in advancing productive change. The Evidence for Improvement (EFI) framework illustrates how improvement networks can be conceptualized and measured using a three-level nested model composed of (1) a working theory of improvement towards achieving their aim, (2) an improvement enterprise that functions as a scientific learning community, and (3) environmental contexts that may constrain or enable improvement efforts.

The paper describes how evidence helps make activities at each of these levels visible, how the role of an analytic partner supports activities at each level, and how leaders and analytic partners need to develop the dispositions, skills and knowledge that enable this work.

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For addition information, watch this session, recorded at the Carnegie Summit in 2021, introducing the Evidence for Improvement Framework: