Artificial Intelligence for Developing Better Patient Scheduling and Predicting Bed Availability in Hospitals
DOI:
https://doi.org/10.54536/ajsts.v5i1.6554Keywords:
Artificial Intelligence, Bed Availability Prediction, Healthcare Efficiency, Hospital Operations, Patient SchedulingAbstract
Optimizing patient scheduling and predicting bed availability in real-time remain paramount challenges for metropolitan hospitals in Bangladesh, especially high population density cities like Dhaka and Chattogram. Manual systems can lead to congestion and lack of efficient resource allocation. The objective of this study was to evaluate the perceptions, readiness, and operational needs for healthcare professionals with the use of Artificial Intelligence (AI) in support of patient scheduling and forecasted bed availability. Goals of the project were to review current scheduling practices, look for patterns in peak demand, create high-brain-pressure units lists, evaluate the perceived value-add of AI and uncover barriers to uptake. A structured quantitative questionnaire was administered to 400 participants among selected hospitals. Descriptive statistics demonstrated the common occurrence of beds shortages, extensive variation in discharge-to-bed-ready time and reliance on manual scheduling systems. There was overwhelming support for the adoption of AI, particularly with regards to AI bed prediction in real-time, decreased time waiting for a bed, better turnover efficiency and automation of administrative tasks. Nevertheless, there were several barriers which consisted of scant digitized data, inadequate training and infrastructural limitation. The study concludes that AI-based scheduling and bed prediction systems can significantly improve productivity, alleviate congestion issues, and help with evidence-based decisions in Bangladeshi hospitals.
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