Staffing Optimization
Customer Company Size
Large Corporate
Region
- Europe
Country
- United Kingdom
Product
- DSS Patient Forecasting System
Tech Stack
- Machine Learning
- API
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
- Employee Satisfaction
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Healthcare & Hospitals
Applicable Functions
- Human Resources
Use Cases
- Predictive Replenishment
Services
- Data Science Services
About The 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.
The 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.
The 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.
Operational Impact
Quantitative Benefit
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