Multiple projects offer us early insight into potential areas of development
About this project:
This project involved predicting breaches in our RTT pathways, using a new cloud-based dataset that our team hadn’t yet investigated.
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Head of Nurture
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Executive Summary
Predictive Model | Breaches in RTT pathways
Objective:
A Nurture student undertook a project to analyse a newly available cloud-based dataset, aiming to predict breaches in Referral to Treatment (RTT) pathways. The goal was to generate early insights, inform ongoing research within the trust, and lay the groundwork for future improvements through data-led decision-making.
Solution:
The student built and tested predictive models using the untested dataset, highlighting key limitations and offering clear recommendations for improvement. While the final model’s performance had room for growth, the process reflected the experimental nature of healthcare analytics and demonstrated a strong grasp of research rigour and analytical methodology.
Results:
- Provides a fresh talent & new perspectives, enabling alternative solutions and cultivates a creative mindset.
- Provides a natural, structured and repeatable framework for developing leadership and management skills.
- Creates a positive & productive work environment, with greater capacity - often a springboard for future research.
Challenge
Solution
Outcome
A Nurture student was tasked with exploring a newly available cloud-based dataset to predict breaches in Referral to Treatment (RTT) pathways.
The project aimed to uncover early insights and support wider research efforts within the trust, forming the foundation for future development and data-driven decision-making.
Early stage research
One of our students was involved predicting breaches in our RTT pathways. They used a new cloud-based dataset that our team hadn’t yet investigated.
Although the performance of the final model could have been improved, the project was extremely well received by the team and the university, because the student correctly identified the limitations and gave suggestions for improvements.
These model failures are really common in our type of research, it’s all just part of the experimentation process.
Experimentation is all part of the process
The project was highly valued by both the trust and the university. The analytical groundwork laid by the student has since informed additional development work that is delivering tangible benefits to the organisation - demonstrating how early-stage research, even with imperfect models, can catalyse meaningful progress in complex data environments.
