Acceptance of AI in Health Care for Short- and Long-Term Treatments: Pilot Development Study of an Integrated Theoretical Model.

Abstract

As digital technologies and especially artificial intelligence (AI) become increasingly important in health care, it is essential to determine whether and why potential users intend to use related health information systems (HIS). Several theories exist, but they focus mainly on aspects of health care or information systems, in addition to general psychological theories, and hence provide a small number of variables to explain future behavior. Thus, research that provides a larger number of variables by combining several theories from health care, information systems, and psychology is necessary. This study aims to investigate the intention to use new HIS for decisions concerning short- and long-term medical treatments using an integrated approach with several variables to explain future behavior. We developed an integrated theoretical model based on theories from health care, information systems, and psychology that allowed us to analyze the duality approach of adaptive and nonadaptive appraisals and their influence on the intention to use HIS. We applied the integrated theoretical model to the short-term treatment using AI-based HIS for surgery and the long-term treatment of diabetes tracking using survey data with structured equation modeling. To differentiate between certain levels of AI involvement, we used several scenarios that include treatments by physicians only, physicians with AI support, and AI only to understand how individuals perceive the influence of AI. Our results showed that for short- and long-term treatments, the variables perceived threats, fear (disease), perceived efficacy, attitude (HIS), and perceived norms are important to consider when determining the intention to use AI-based HIS. Furthermore, the results revealed that perceived efficacy and attitude (HIS) are the most important variables to determine intention to use for all treatments and scenarios. In contrast, abilities (HIS) were important for short-term treatments only. For our 9 scenarios, adaptive and nonadaptive appraisals were both important to determine intention to use, depending on whether the treatment is known. Furthermore, we determined R² values that varied between 57.9% and 81.7% for our scenarios, which showed that the explanation power of our model is medium to good. We contribute to HIS literature by highlighting the importance of integrating disease- and technology-related factors and by providing an integrated theoretical model. As such, we show how adaptive and nonadaptive appraisals should be arranged to report on medical decisions in the future, especially in the short and long terms. Physicians and HIS developers can use our insights to identify promising rationale for HIS adoption concerning short- and long-term treatments and adapt and develop HIS accordingly. Specifically, HIS developers should ensure that future HIS act in terms of HIS functions, as our study shows that efficient HIS lead to a positive attitude toward the HIS and ultimately to a higher intention to use.