Hospital Staffing Optimization

In order to adapt staffing to daily needs, data platforms like Dataiku can drive better anticipation of patient demand (through patient forecasting), ensuring that there are enough healthcare providers to support the patient load.

A hospital depends on its staff: not enough people will negatively affect patient experience and quality of care, while too many will hinder its financial stability. As staffing accounts for more than 50 percent of an average hospital’s costs, it is crucial to manage staffing wisely. But how does one efficiently manage these costs while schedules are for the most part still done by hand, based on the number of available beds?

Dataiku worked with a major hospital on a use case to optimize staffing based on patient forecasting, which they were able to complete in just three months.

 

Challenge: Staffing Inefficiency, Frustration, and High Costs

Physician overwork and patient dissatisfaction are typically the result of a lack of data-driven decision making during the staffing process. Inefficient allocation of staffing hours impeded organizations’ ability to deliver optimal care and retain the best doctors.

This particular hospital needed to better anticipate patient volumes so that staffing decisions could be made in a more transparent fashion that would not undermine providers – that meant developing a more accurate prediction of staffing needs. For this, the hospital wanted to find a technical solution that would enable it to:

  • Model patient inflows on a small scale and
  • Recommend staffing schedules based on patient demand forecasting.
Leverage Patient Data in 15 minutes with Dataiku Watch Video

Solution: An Automated Predictive Application to Forecast Patient Demand

Demand forecasting is typically performed by looking back at historical patient demand data and projecting trend lines with a seasonal adjustment. In order to adapt its staffing needs on a daily basis, the hospital worked with Dataiku to better anticipate patient demand by building and implementing a patient forecasting system application.

First, the application automatically compiles and processes internal and historical data as well as external datasets such as weather, national epidemics, holidays, and traffic. Then, a machine learning algorithm builds a statistical model that forecasts patient demand; this prediction is continually improved as new data is incorporated into the model. Finally, an API links the predictive model to the staffing schedule system. Staffing managers therefore have updated staffing suggestions in their scheduling tool based on time, date, and department.

“Staffing optimization is the most important lever we have to control our costs. With Dataiku, we managed to enter a new dimension in terms of patient forecasting, which enabled us to have a much more accurate view of who was needed when and where. Plus, our team is really enjoying using the tool – we’re looking forward to building more applications in Dataiku.”

 

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Results: Significant Decrease in Staffing Cost and Turnover

Thanks to optimized schedules, the hospital can deliver better care to its patients and has significantly improved productivity:

  • The Dataiku-run predictive analytics models are 47% more accurate than historical average predictions
  • 11% decrease in staffing costs, saving around $730k per year
  • An estimated 9% decrease in staffing turnover in year 1 since application deployment.

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