ADHERENCE

Underway
03/06/2023

-

28/02/2025

Main objectives

This project aims to:
- develop methods and tools to target the level of therapeutic adherence in patients with chronic diseases;
- target clinical evolutions of these pathologies in order to evaluate a potential association between adherence level and clinical evolution.
Adherence is studied in the context of the following three types of chronic disease: cancers treated with oral anticancer therapies, hypertension (HT) and its treatments, and solid organ transplants treated with immunosuppressants.

Challenge

Therapeutic adherence (also known as compliance) describes the degree to which patients comply with medical prescriptions. In France, adherence rates vary around 50%, depending on the disease and type of treatment. This complex, multifactorial phenomenon has both negative clinical consequences for the patient (in terms of prognosis and quality of life), and negative economic consequences for society. According to the World Health Organization, lack of therapeutic adherence is the main reason why patients do not obtain the maximum benefit from their treatments, compared with expectations resulting from clinical trials. The problem of non-adherence, though long-standing, is particularly critical today with the increasing prevalence of chronic diseases. Improved management of chronic diseases means prescribing multiple treatments that are complex to follow, and patients are not always aware of what is at stake. For these reasons, improving adherence to treatment is a highly topical issue, in order to support therapeutic progress and optimize the quality of care for patients with chronic diseases.

Methodology/Technology used

Information extraction, supervised learning, classical statistical methods (correlation, association, etc.)

Contacts

Adrien Coulet () & Brigitte Sabatier ()

Members

Inria, AP-HP, Inserm, Université de Paris

Other projects

COVIPREDS

Description

US Caractérisation et prédiction de la survenue de formes graves ou létales du COVID-19 à partir des données issues de l’EDS de l’AP-HP

Names of partners involved
AP-HP, Inria & Centrale Supélec

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