Noshad Rahimi

Explaining health technology adoption: Past, present, future

One of the most pressing challenges of healthcare innovation today is the lack of technology adoption. Research that improves our ability to understand, predict, and advance technology adoption in health care needs to be based on well-tested theories. With the interest to conduct high quality research in health technology adoption in future, this study reviews the theories used in this context to either identify the superior theory(ies) and or discover the issues that need resolution for improving future HTA researches. To do that, the most popular [1][2] social cognitive theories conceived over the past four decades are reviewed analytically from the perspective of their capacity to explain, predict and intervene in health technology acceptance, adoption and adherence. While all these theories are instrumental in conducting adoption studies, and some like UTAUT (Unified Theory of Acceptance and Use of Technology) are better than others at it, there is no perfect theory to study HTA. Literature repeatedly suggests that while utilizing general theories that have successfully passed the test of time could serve as a strong foundation, there is a compelling need for new and more empirical theories. There is a need for health researchers to expedite theoretical evolution by conducting comprehensive observation and rigorous evaluation to 1) manipulate and expand existing theories and or 2) create new theories that better address the specific needs and challenges of health technology application to enhance the utility and better reflect empirical findings. The structure of this paper is as follows. After summarizing the specifics of health technology innovations, the primary challenges in its acceptance are categorized. From there the body of this paper is dedicated to the review of most popular social cognitive theories, as depicted in Figure 1, from: 1) general human behavior repeatedly applied in healthcare studies and rooted HTA researches, and 2) theories dedicated to the study of technology acceptance behavior and applied as the prominent theories in studying HTA. Each theory is reviewed, followed by examples of its applications especially in modeling health technology adoption (HTA) behavior. Each theory is then evaluated based on the salient factors involved in the study of technology innovation in healthcare space in addition to the classical influencing concepts in technology adoption behavior. In the discussion section, these theories are compared and the applications studied are synthesized in the attempt to identify some of the best theories and state of the art practices used in the study of HTA. The conclusion section summarizes the findings of the literature and recommends best approaches for conducting empirical studies and planning effective processes that stimulate theoretical evolution in HTA and facilitate enhancement of acceptance of health technology innovations.

Soft Data Analytics with Fuzzy Cognitive Maps: Modeling Health Technology Adoption by Elderly Women

Modeling how patients adopt personal health technology is a challenging problem: Decision-making processes are largely unknown, occur in complex, multi-stakeholder settings, and may play out differently for different products and users. To address this problem, this chapter develops a soft analytics approach, based on Fuzzy Cognitive Maps (FCM) that leads to adoption models that are specific for a particular product and group of adopters. Its empirical grounding is provided by a case study, in which a group of women decides whether to adopt a wearable remote healthcare monitoring device. The adoption model can simulate different product configurations and levels of support and provide insight as to what scenarios will most likely lead to successful adoption. The model can be used by product developers and rollout managers to support technology planning decisions.