AI-Driven Patient Flow Management in Hospitals: Reducing Wait Times and Enhancing Care

Patient flow management for hospitals is a key effort to reduce hospital wait times and to optimally allot resources. In this research we focus on using Machine Learning algorithms like reinforcement learning, genetic algorithms, deep learning etc. to drive efficiency in hospitals implement. The predictive models were developed based on real hospital datasets, and are used to improve patient scheduling, bed management, and prognosis of hospital stay. Results from experimental work showed that waiting times of patients can be reduced by 37.5%, and the bed occupancy efficiency can be improved by 29% with AI-driven scheduling and optimized resource allocation. Furthermore, predictive models produced an 87.2% accuracy prediction of patient hospital stay durations above traditional statistical methods by 18%. The responses of the models were compared to related works and their flexible nature to evolving healthcare environments was noted. Although large scale implementation remains a challenge, key barriers continue to include data privacy as well as system integration and clinician acceptance. This study brings out the importance of improved cybersecurity frameworks and real-time AI interpetability to facilitate hospital integration seamlessly. Second, future research should capitalize upon real-time monitoring and the blockchain based security integration with the real time decision support systems provided by AI. AI’s ability to transform healthcare with more effective, data driven and patient needs responsive patient flow management is underlined by these findings.