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Continuous predicted risks should be retained when deploying clinical prediction models.

Robin Blythe¹, Rex Parsons², Marcus E H Ong³

¹Programme in Health Services Research & Population Health, Duke-NUS Medical School, Singapore.

Journal of Clinical Epidemiology

|October 8, 2025

Summary

This summary is machine-generated.

Using continuous risk scores, not just risk groups, improves patient prioritization and economic value in healthcare settings. Ranking patients by predicted risk offers significant benefits, especially under resource constraints.

Keywords:

Clinical prediction models Health economics Machine learning Sensitivity and specificity

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Area of Science:

Medical Informatics
Health Services Research
Clinical Decision Support

Background:

Clinical prediction models often dichotomize probabilities into risk groups, potentially losing valuable information.
The economic implications of using continuous risk predictions versus discrete risk groups are not well understood.

Purpose of the Study:

To evaluate the impact of ranking patients by continuous predicted risks compared to using risk groups alone.
To assess the economic value and performance benefits of continuous risk prediction under varying conditions.

Main Methods:

Simulated scenarios with different model discrimination and event prevalence.
Evaluated performance using positive predictive value, sensitivity, and mean rank of true positives.
Applied findings to a machine learning-based ordinal scoring system using real emergency department data.

Main Results:

Ranking patients by predicted risk demonstrated performance benefits over using risk groups alone.
Benefits increased with higher model discrimination and outcome prevalence, and were robust to poor calibration.
Analysis of Singaporean emergency department data showed greatest benefits under high resource constraints.

Conclusions:

Prioritizing patients using continuous probabilities within risk groups offers potential economic advantages.
Future prediction models should provide equations for continuous risk scores for better patient prioritization.
Integrating continuous risk scores with clinical judgment is recommended for deployed models.