CHILLI publication
Predictive Analytics for Earlier Identification of Healthcare Risk
CHILLI Research Seminar Series
Publication overview
Abstract
This presentation explores how routinely collected healthcare data and predictive modelling may support earlier identification of patient or service-level risk. It considers data quality, model evaluation, clinical usefulness, fairness, interpretability, and the importance of avoiding unsupported conclusions from predictive outputs.
About this publication
Predictive analytics may help healthcare teams identify emerging risks earlier, prioritise attention, and support more timely decision-making.
This presentation examines the use of routinely collected healthcare data in predictive modelling and considers the practical challenges associated with data quality, bias, model performance, interpretability, and clinical integration.
It emphasises that predictive outputs should support—not replace—professional judgement and that evaluation must consider clinical usefulness, fairness, safety, and real-world implementation.