Measuring Innovation Adoption in Healthcare

Diffusion of Innovation theory provides a useful framework for studying the adoption process. Diffusion studies have found that specific attribute perception is critical for innovators and early adopters and account for 49-87% of the variance on whether those individual adopt an innovation or not. Perceived attributes of an innovation include: Relative advantage—the degree to which an innovation is perceived as better than the idea it supersedes (p. 250). The higher the perceived relative advantage, the more likely the innova- tion will be adopted. d Compatibility—the degree to which an innova- tion is perceived as consistent with the existing values, past experiences and needs of potential adopters (p. 250). If the innovation is perceived as an extreme change, then it will not be compatible with past experiences and is less likely to be adopted. d Complexity—the degree to which an innovation is perceived as relatively difficult to understand and use (p. 250). Innovations that are perceived as complex are less likely to be adopted. d Observability—the degree to which the results of an innovation are visible to others (p. 251). If the observed effects are perceived to be small or non-existent, then the likelihood of adoption is reduced. d Trialability—the degree to which an innovation may be experimented with on a limited basis (p. 251). This may include trying out parts of a program or having the opportunity to watch others using a new program. Trialability is positively related to the likelihood of adoption. We want to look at Innovation theory within the context of implementing Artificial Intelligence (AI) within the healthcare vertical. AI is still relatively new and innovative in the healthcare space. A framework to analyze the potential of adoption within healthcare providers is critical to gain widespread adoption, where the technology would begin to enter critical mass.        

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