The adoption of Machine learning (ML) in the healthcare supply chain can help the supply chain to make better decisions on the inventory, making the supply chain operations efficient. But in the United States, the establishment of Machine learning (ML)is not easy because of the higher level of regulations, higher cost, data privacy issues and issues related to the integration of Machine learning (ML) with the existing systems. This article seeks to discuss the problems and possibilities of ML’s implementation in American healthcare supply chains while considering factors that facilitate or hinder the processes. In this study, national survey data of 200 professionals from the healthcare supply chain are used to discover the challenges related to the implementation of ML and the respondents’ perceptions of the advantages and future of ML. Comparing with the result of questionnaire survey, data analysis points out that Health Insurance Portability and Accountability Act (HIPAA) and Food and Drug Administration (FDA) are two important regulatory that create problems for health IT firms, including security requirements and high compliance cost. The research also reveals that geographic location influences ML priorities, as Machine learning (ML) is expected to enhance decision making for the Western US participants while cost containment applications of ML are valued by Northeast participants since healthcare costs are higher in this region. The study also shows that there are differences based on the role; procurement managers and supply chain analysts have varied opinions regarding the use of ML to improve cost efficiency and inventory management. The study shows that there is a need to develop the U.S.-specific approaches to ML adoption based on the regional, regulatory and role-specific circumstances. Some are increasing data accuracy and data security; others are implementing specific monetary rewards for patients; still, others are investing in staff development and infrastructure to support the use of ML. This research serves to add to the developing literature on ML in healthcare supply chains and gives a viewpoint unique to the United States while also delivering informative best practices for those organizations aiming to exploit ML’s possibilities effectively

Machine learning, healthcare supply chain, U.S. healthcare, regulatory compliance

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