Keywords

diffusion of innovation, digital health, dissemination, health care system, implementation science, institutional theory

 

Authors

  1. Scarbrough, Harry
  2. Kyratsis, Yiannis

Abstract

Issue: In broad terms, current thinking and literature on the spread of innovations in health care presents it as the study of two unconnected processes-diffusion across adopting organizations and implementation within adopting organizations. Evidence from the health care environment and beyond, however, shows the significance and systemic nature of postadoption challenges in sustainably implementing innovations at scale. There is often only partial diffusion of innovative practices, initial adoption that is followed by abandonment, incomplete or tokenistic implementation, and localized innovation modifications that do not provide feedback to inform global innovation designs.

 

Critical Theoretical Analysis: Such important barriers to realizing the benefits of innovation question the validity of treating diffusion and implementation as unconnected spheres of activity. We argue that theorizing the spread of innovations should be refocused toward what we call embedding innovation-the question of how innovations are successfully implemented at scale. This involves making the experience of implementation a central concern for the system-level spread of innovations rather than a localized concern of adopting organizations.

 

Insight/Advance: To contribute to this shift in theoretical focus, we outline three mechanisms that connect the experience of implementing innovations locally to their diffusion globally within a health care system: learning, adapting, and institutionalizing. These mechanisms support the distribution of the embedding work for innovation across time and space.

 

Practical Implications: Applying this focus enables us to identify the self-limiting tensions within existing top-down and bottom-up approaches to spreading innovation. Furthermore, we outline new approaches to spreading innovation, which better exploit these embedding mechanisms.