Machine Learning in Public Health
Understand what influences hospital cost
A steadily climbing health expenditure has resulted in a more complex budgeting process. The budget team needed a mechanism to better understand what influences hospital cost and length of stay, two of the most imperative factors when deciding the future budget.
“Managing the scare resources of staffing and facilities is an ongoing challenge for any health service. This data-driven approach enables more effective planning and utilisation so more patients can be supported.”
Too many variables to consider
One of the most challenging tasks the budget team faces is deciding how many factors to consider when building a new budget plan. There
How to better forecast?
The budget team relied on simple forecasting method for creating a budget plan. They assess what occurred in the past, and makes a reasonable extension to estimate what occurs next. Such method did not provide adequate accuracy to respond to a complex and changing environment such as public health system.
Machine Learning helped understand what affects cost
AgileBI built a predictive model that helped identify what factors most affect the cost, and by how much they have influenced on key factors such as hospital length of stay. We used the latest advancement in the Machine Learning field known as a neural network for developing the model.
We created an interface in Power BI for the budget team to interact with the model. In this interface, the team can configure various factors, such as in which hospital the patient receives treatment, it will perform simulation using the inputs and display the outcome graphically.
Results and future plans
The team now have a comprehensive set of tools for performing budget and forecast. They can now better understand influential factors to the cost and length of hospital stay.
The next step is to incorporate external data such as weather, census and economics. This makes the model more complete when forecasting the cost.