AN INVESTIGATION INTO THE IMPACT OF AI ON HEALTHCARE RESOURCE ALLOCATION AND PATIENT FLOW MANAGEMENT

Abstract

This mixed-method study examined the role of artificial intelligence in healthcare resource allocation and patient flow management by using twelve healthcare facilities. The study included 380 medical workers by questionnaires and 30 major stakeholders by interview to investigate the effects of AI implementation between 2021 and 2024. Results found that AI-based systems were far more efficient in the use of resources with a 34-percent increase, for the reduction of patient waiting time an average of 28 minutes, and staff productivity by 22 percent. Public hospitals were more successful in their resource allocation optimization, whereas patient flow management was more successful in the area of pruriency. The major fields of AI application were predictive analytics on bed management, automated scheduling and real-time resource tracking platforms. Quantitative data demonstrated that there are high levels of correlations between the level of AI implementation and the levels of operational efficiency. Qualitative results were found to be critical success factors such as training of the staff, leadership support, and capability of integrating a system. Nevertheless, there were issues such as high costs of initial implementations, employee resistance to adoption of the technology and quality of data. The research came to the conclusion that AI technologies may be of significant use to a healthcare operational efficiency, yet the successful implementation process cannot be done without the dense change management policies and long-term organization dedication. The results are relevant to the transformative nature of AI in healthcare operations and can become practically important to healthcare administrators and policymakers who may need to adopt AI to enhance effectiveness in the delivery of care and resources management