Automated demand forecasting makes staff rostering less stressful and fastHealthcare Industry
This fundamental shift in demand affected the downstream work of staffing the nurses appropriately. They had to revise their entire demand forecasting. FOYI was involved for this work.
Challenge
The nurse staffing customer initially proposed a project scope with the task of building a rostering application to enable them to roster the nurses automatically. Over the course of initial discussion, FOYI was able to highlight the key challenges with that project scope.
1. The pre-pandemic historical data is less relevant.
The rostering process is dependent on the demand for a staff per hospital, ward and shift. Since this demand is now different during pandemic, the earlier estimates do not work.
2. The pandemic data is only 6 months old and therefore lacks seasonality.
The operations team are used to observing seasonality in the data. For example, the winter months had a few wards requiring more staff than the other. However, since the pandemic started 6 months ago, there was not enough data to see how these known estimates have changed.
3. Some nurses were staffed between in-patient and out-patient wards.
The number of in-patients were dramatically low for some wards and the number of staff needed for the out patients dramatically increased on account of longer and more complex process of segregating COVID symptoms and the rest. This increased the unpredictability of required staffing for in-patient vs out-patients.
Approach
As a first step, FOYI proposed an exploratory data analysis project be undertaken first. The objective was to identify the insights from the 6 months of pandemic data in comparison to the pre-pandemic data. This project can then help in defining the scope of demand forecasting project.
Once the demand forecasting model is in place, this could then be the key input to the customer’s actual request i.e. staff rostering application.
This proposed approach led the customer to start with two foundational projects before the rostering application project.
Outcome
The 3 key deliverables of this approach and their benefits are as follows.
1. Understanding the parts of the business that changed more than the rest
Benefit: Identifying the outliers meant more efficient and effective use of the project budget.
2. User driven dynamic application to generate the staffing demand per hospital, ward and shift
Benefit: An on-demand training and forecasting application meant that the application lends itself to any further changes to the demand.
3. Detailed documentation to maintain the model and the application.
Benefit: No dependency on external consultants for retraining the model.