Dataiku Case Studies Staffing Optimization

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	Staffing Optimization Dataiku

Staffing Optimization

Dataiku

Technology Category	Analytics & Modeling - Predictive Analytics
Applicable Industries	Healthcare & Hospitals
Applicable Functions	Human Resources
Use Cases	Predictive Replenishment
Services	Data Science Services
Challenge	A major healthcare provider in the UK was struggling with staffing inefficiencies, leading to physician overwork, patient dissatisfaction, and high costs. The hospital's staffing process was largely manual and based on the number of available beds, which did not allow for efficient allocation of staffing hours. This lack of data-driven decision making was impeding the hospital's ability to deliver optimal care and retain the best doctors. The hospital sought a technical solution that would enable it to model patient inflows on a small scale and recommend staffing schedules based on patient demand forecasting. Read More
About Customer	The customer is a major healthcare provider in the UK. It employs approximately 1700 people and is responsible for delivering healthcare services to a large population. The hospital is committed to providing the best possible care to its patients, which requires efficient and effective staffing. However, the hospital was struggling with staffing inefficiencies, which were leading to physician overwork, patient dissatisfaction, and high costs. The hospital sought a solution that would enable it to better anticipate patient volumes and make staffing decisions in a more transparent and efficient manner. Read More
Solution	The hospital partnered with DSS to build and implement a patient forecasting system application. The application automatically compiles and processes internal and historical data as well as external datasets such as weather, national epidemics, holidays, and traffic. A machine-learning algorithm then builds a statistical model that forecasts patient demand. This prediction is continually improved as new data is incorporated into the model. An API links the predictive model to the staffing schedule system, providing staffing managers with updated staffing suggestions in their scheduling tool based on time, date, and department. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Predictive Analytics

Applicable Industries

Healthcare & Hospitals

Applicable Functions

Human Resources

Use Cases

Predictive Replenishment

Services

Data Science Services

Challenge

A major healthcare provider in the UK was struggling with staffing inefficiencies, leading to physician overwork, patient dissatisfaction, and high costs. The hospital's staffing process was largely manual and based on the number of available beds, which did not allow for efficient allocation of staffing hours. This lack of data-driven decision making was impeding the hospital's ability to deliver optimal care and retain the best doctors. The hospital sought a technical solution that would enable it to model patient inflows on a small scale and recommend staffing schedules based on patient demand forecasting.

About Customer

The customer is a major healthcare provider in the UK. It employs approximately 1700 people and is responsible for delivering healthcare services to a large population. The hospital is committed to providing the best possible care to its patients, which requires efficient and effective staffing. However, the hospital was struggling with staffing inefficiencies, which were leading to physician overwork, patient dissatisfaction, and high costs. The hospital sought a solution that would enable it to better anticipate patient volumes and make staffing decisions in a more transparent and efficient manner.

Solution

The hospital partnered with DSS to build and implement a patient forecasting system application. The application automatically compiles and processes internal and historical data as well as external datasets such as weather, national epidemics, holidays, and traffic. A machine-learning algorithm then builds a statistical model that forecasts patient demand. This prediction is continually improved as new data is incorporated into the model. An API links the predictive model to the staffing schedule system, providing staffing managers with updated staffing suggestions in their scheduling tool based on time, date, and department.

Impact #1	The hospital was able to deliver better care to its patients thanks to optimized schedules.
Impact #2	The hospital saw a significant improvement in productivity.
Impact #3	The hospital was able to decrease staffing costs by 11%, saving around $730k per year.

Impact #1

The hospital was able to deliver better care to its patients thanks to optimized schedules.

Impact #2

The hospital saw a significant improvement in productivity.

Impact #3

The hospital was able to decrease staffing costs by 11%, saving around $730k per year.

Benefit #1	The DSS run predictive analytics models are 47% more accurate than historical average predictions.
Benefit #2	The hospital was able to decrease staffing costs by 11%, saving around $730k per year.
Benefit #3	The hospital saw an estimated 9% decrease in staffing turnover in the first year since the application's deployment.

Benefit #1

The DSS run predictive analytics models are 47% more accurate than historical average predictions.

Benefit #2

The hospital was able to decrease staffing costs by 11%, saving around $730k per year.

Benefit #3

The hospital saw an estimated 9% decrease in staffing turnover in the first year since the application's deployment.

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Overview

Staffing Optimization

Operational Impact

Quantitative Benefit