# The hidden risk of round numbers and sharp thresholds in clinical practice

## TL;DR

This paper shows how using round-number thresholds in clinical decisions can create misleading risk assessments and suggests more nuanced methods for better patient outcomes.

## Contribution

The paper introduces an interpretable machine learning model to identify anomalies from threshold-based clinical practices.

## Key findings

- Round-number thresholds can create discontinuities in mortality risk assessments.
- Threshold-based practices lead to counter-causal paradoxes in risk evaluation.
- Dynamic risk assessment methods are needed to align with the continuous nature of risk.

## Abstract

Clinical decision-making often simplifies continuous risk data into discrete levels using round-number thresholds. These simplifications can distort risk assessments. To systematically uncover these distortions, we develop an interpretable machine learning model that identifies anomalies caused by threshold-based practices. Through simulations, real-world data, and longitudinal studies, we demonstrate how round-number thresholds can lead to discontinuities and counter-causal paradoxes in mortality risk. Despite advances in medicine, these anomalies persist, underscoring the need for dynamic and nuanced risk assessment methods in healthcare. Our findings suggest continuous reassessment of clinical protocols, especially in critical settings like intensive care, to improve patient outcomes by aligning thresholds with the continuous nature of risk.