Optimisation des parcours patients pour lutter contre l'errance de diagnostic des patients atteints de maladies rares

TL;DR

This paper proposes a probabilistic model and simulator to optimize patient pathways for rare disease diagnosis in France, aiming to reduce diagnostic delays and improve referral timing to specialized centers.

Contribution

It introduces a novel probabilistic modeling approach and a simulation tool to detect wandering patients and optimize their referral to specialized centers for rare diseases.

Findings

Model accurately predicts patient wandering patterns

Simulator identifies optimal referral strategies

Potential to reduce diagnosis delays significantly

Abstract

A patient suffering from a rare disease in France has to wait an average of two years before being diagnosed. This medical wandering is highly detrimental both for the health system and for patients whose pathology may worsen. There exists an efficient network of Centres of Reference for Rare Diseases (CRMR), but patients are often referred to these structures too late. We are considering a probabilistic modelling of the patient pathway in order to create a simulator that will allow us to create an alert system that detects wandering patients and refers them to a CRMR while considering the potential additional costs associated with these decisions.