Improving hospital resource management using explainable LOS prediction model

Effective management of hospital resources is critical in delivering high-quality care while keeping costs under control. One aspect of resource management involves the understanding of the length of stay of patients, referred to as Length of Stay (LOS). Understanding LOS helps the hospital manage its patient population more effectively and ensure efficient and effective care. Unfortunately, traditional statistical models fail to effectively deal with the intricacies of the data obtained from the hospital’s electronic records. This research aims to introduce an effective and efficient framework for making LOS predictions using the power of machine learning models such as XGBoost and LSTM. To ensure the model’s predictions are accurate and reliable, the framework has been integrated with the capabilities of explainable AI, which helps in understanding the decision-making process of the model, achieved with the integration of SHAP. The framework has been developed with two primary dashboards: Admin Dashboard and Patient Dashboard. The Admin Dashboard has been created with the primary objective of assisting the hospital’s staff in effectively utilizing the capabilities of the framework.On the other hand, the Patient Dashboard gives patients a sense of empowerment by allowing them to have a safe place to store medical records, view test results, monitor recovery progress, schedule appointments, and even communicate with an AI-based health assistant.The integration of complex analytics, transparent AI explanations, visualization tools, and patient-centric tools is intended to enhance decision-making in hospitals, patient engagement, and efficient resource management in healthcare.