Build a machine learning model to forecast the bed occupancy rate in hospitals from the history of patient admissions, discharge records, and seasonality. To develop a predictive analytical solution to optimize resource allocation, staffing level resources, and bed capacity management to ensure efficient ways of health care delivery with reduced wait times for patients.
The project came about from the urge to enhance the running and patient care in a hospital through data-driven decisions. It brings together some experts in data scientists and healthcare analysts, who work in collaboration with hospital administrators and IT professionals.
Week 1-2: Initial Planning and Requirement Analysis
Define the objectives of the project, scope and high-level requirements.
Gathering of necessary data and resources.
Week 3-5: Data Collection and Preprocessing Phase
Gathering/collecting and pre-processing historical data about patient admission, discharge records, and seasonal trends.
Extract data features relevant to the facets of the forecasting model.
Week 6-8: Model Development and Training Phase
Developing and training the Machine Learning model for Hospital Bed Occupancy Rate Forecasting.
Model Accuracy and Performance Validation using Historic Data.
Week 9-10: Solution Integration and Testing Phase
Implementation of a predictive analytics solution for the optimization of hospital resources.
Integrate the solution with existing hospital management systems
Test and refine the solution based on performance metrics and user feedback Week 11-12: Deployment and Monitoring Phase
The solution shall be deployed in the hospital environment.
Follow up on model performance and further adjust it as required.
