Prediction Of Nursing Requirement in a Multi-Specialty Hospital

Objectives

Arrive at the best-case scenario for the number of nurses required to meet the patients demand while minimizing the cost to the hospitals. This decision is to be made based on the historical data and the regulatory guidelines of ideal nursing capacity. Outcomes

With our highly interactive prediction dashboards, our model could tell the number of nurses required in the hospital based on its occupancy. This helped the management team to plan their manpower requirements in advance. With a “What if?” prediction method it helped in understanding the staffing level under different circumstances better.

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Simulation to estimate the nurses required in a hospital for its daily operation and the nurses required in a newly built hospital based on Regulatory guidelines.

In a hospital, nurses can account for over 50% of the staffing resources and their allocations to different departments and shifts significantly affects the quality of the care provided to patients. Short staffing of nurses can result in compromise with patient care and a significant over-time burden on nurses. Over-staffing can lead to higher employee cost to the organization. So, a balance in the two is required for smooth operation of the hospitals.

Traditional way of balancing under and over staffing

Traditionally, a nursing manager estimates the nurses required to be staffed and the rostering pattern for each shift. These are two different but interrelated decisions, which rely solely on nursing manager’s ability to quantify nursing workload. While this typical guesstimate utilizes the experience of the senior nurse, multiple factors and variations in parameters lead to wrong assessment of the requirements. Further, the costing factor is seldom a concern for the Nursing Manager due to the lack of visibility of its impact on the bottom-line.

Another approach typically used to employ nurses is based on broad nurse to patient ratio. The drawback of using such an assumption is that all patients do not have similar needs and hence their requirements vary.

Without enough buffers and algorithms in place, the daily ad-hoc rostering led to considerable loss of morale and overtime pays amongst the nursing staff.

Data Analytics helps management predict the Nursing requirement accurately

Using the regulatory guidelines of nursing requirements based on occupancy as inputs along with OP footfalls, daycare services, requirements in labs & blood banks, number of operational shifts, we have developed an AI based forecasting model for different parameters. These include nursing availability such as leave planning, overtimes, occupancy, and service volumes in the hospital. The different parameters used in the model are forecasted using historical data residing in the repositories using a time series forecasting technique.

The model, by default, runs multiple simulations on the nurse requirement at current occupancy, average occupancy, peak occupancy and full occupancy in the hospital. These ensure that all scenarios are covered in planning.

Simultaneously, the designed model also has provision to set the expected occupancy in different wards, an estimated patient footfall in the Outpatient areas and a healthy overtime logic to arrive at the numbers required in a newly built hospital. The dashboard is a single go-to solution for all nursing planning and rostering requirements for healthcare providers ranging from small clinics to larger health cities

Effective planning helps better patient care and cost benefits

Since this highly-interactive model takes all factors affecting the requirement into consideration including a healthy overtime limit, it has helped hospitals across the healthcare provider network to plan its staffing better and improve clinical outcomes

The gap between under and over-staffing has been minimized across the units. Management can use “What if?” analysis by modifying various parameters and visualize the impact of different scenarios on their nurse staffing level. This helps in better decision making and hiring of additional nursing staff when a surge in patient inflow is expected. It also tells when nursing overtime pay is high and the predicted patient inflow is low, the hiring process for additional nurses can be put on hold and that the hospital can run with current staffing level.

By maintaining appropriate staffing levels throughout the year, the employee costs have been brought in sync with the patient footfalls and occupancy. Simultaneously, nurse satisfaction levels improved considerably due to appropriate workload and shift allocation to each of them.