Cloud-Enabled Patient Acuity Prediction Utilizing Decision Trees in Smart ICU Nurse Assignment Systems

This research introduces a new method for improving Intensive Care Units (ICUs) nurse assignment using cloud computing and Decision Trees (DTs )-based patient acuity prediction models. Effective and timely care delivery is dependent on the efficient allocation of nursing resources, which is becoming more important due to the growing complexity of patients' situations in ICUs. To integrate patient data from many sources seamlessly the proposed system uses cloud infrastructure to process and analyze data in real-time. To proactively identify high-risk situations, use DTs algorithms to forecast patient acuity levels using a variety of clinical indicators. Optimizing staffing levels and improving patient outcomes which incorporates these predictions into the nurse assignment process. Through a case study performed in a real-world ICUs improves nurse efficiency and the quality of patient care. The findings of this study highlight the promise of decision support systems hosted in the cloud for improving the delivery of healthcare in ICUs.