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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