The Pre-implementation Process of AI in the U.S Health Care Industry and how to Overcome Potential Barriers for AI Companies throughout the Process

Abstrakt

This paper aims to analyze why it is crucial for AI companies to overcome potential barriers when entering the U.S health care market and furthermore how to overcome these barriers to get the most efficient pre-implementation process. A case study method was applied with an abductive research approach to answer the research question. Hence, eight informants within the field of AI were interviewed. Barriers within the pre-implementation process of AI were identified through a qualitative analysis of the collected data. It is stated that barriers will occur due to the fragmented industry within the U.S. Moreover, recognizing that these barriers create difficulty when entering the U.S healthcare market due to the lack of transparency in the regulatory field and the late adoption of AI implementation in the healthcare industry. The theoretical foundation of this paper is The Consolidated Framework for Implementation Research, which is developed to guide systematic assessment of multilevel implementation to identify barriers that might influence an intervention implementation and effectiveness. The rationality of going in depth with the pre-implementation process is for AI companies to manage change prior the final implementation of the AI technology. The analysis of the collected data opened the opportunity to create our own framework: The consolidated model for the pre-implementation process of AI technology, which consisting of four categories for AI companies to go through (I.e., Data, Development, Governance, Deployment). The model aims to guide how AI companies can overcome barriers when addressing the U.S. health care industry to get the most efficient pre-implementation process. It is concluded that overcoming barriers when addressing the U.S. health care market is crucial due to the importance of high efficiency within the implementation process of AI. Higher efficiency will lead to less time-consuming processes of approval, more cost-efficiency and better health outcomes. AI companies should acknowledge each category in the defined model to overcome the identified barriers to acknowledge innovation. Lastly the model illustrates the importance of continuous learning as a ground rule for companies to consider throughout their AI implementation journey.