At the heart of every dedicated healthcare provider is the desire to create a system that is truly patient centric. One of the most crucial elements in providing complete care is optimal management of patient flow. If patient flow is not organized around available resources and admission patterns, it could spell utter chaos – creating bottlenecks and adding undue stress on the hospital staff.
Since hospitals consist of interconnected and interdependent units, managing and optimizing patient flow begins with a clear and accurate understanding of the demand for hospital staffing and resources. By leveraging historical data, machine learning models can help hospitals to predict the patient flow at each department, enabling them to optimize resource allocation, minimize wait times, and improve patient outcomes.
The paper addresses critical aspects of managing patient inflow, which include:
- How can healthcare institutions tackle the challenge of patient overcrowding?
- What can hospitals do to avoid staff burnout due to inefficient scheduling?
- How can machine learning models improve hospital operations and patient outcomes?
- What are some of the successful case studies of hospitals using predictive models?