Problem.

Recruiting the nation’s best healthcare workers is just the beginning for Health Education England. The institution trains employees for seven years, investing as much as £250,000 into the careers of junior doctors. Reducing turnover is critical. HEE needed help spotting the career moments where they could provide much-needed support.

Together, we made some major breakthroughs.

Solution.

Predicting dissatisfaction - We helped HEE understand employee attrition dynamics over time and across a variety of development journeys—going way beyond high-level workforce analytics.

Building a unified data warehouse​ - We ingested over 10 million records from multiple NHS and HEE systems, creating a scalable platform that data scientists across departments can use collaboratively.

Reinforcing confidence in data​ - We collected, cleaned and collated data from disparate sources to develop a machine learning model that predicts attrition throughout the career journey.​

Here's what we focused on:

• Quantitative research: for a more in-depth understanding of attrition factors and potential intervention approaches.
• Data management: to create a centralised repository of employee information, making it easier to reuse models to improve performance and reduce time and cost to create insights.
• Predictive modelling: identifying churn signals for each trainee from the start of ​each placement and beyond. 

Results.

• 63% Accuracy identifying individuals at risk of attrition​ ​
• 10m Records ingested from multiple sources​.