Organisation/Company: CNRS
Department: Paris Jourdan Sciences Economiques
Research Field: Economics
Researcher Profile: First Stage Researcher (R1)
Country: France
Application Deadline: 3 Dec 2024 - 23:59 (UTC)
Type of Contract: Temporary
Job Status: Full-time
Hours Per Week: 35
Offer Starting Date: 1 Jan 2025
Is the job funded through the EU Research Framework Programme? Not funded by a EU programme
Is the Job related to staff position within a Research Infrastructure? No
Offer Description Improving our healthcare system to meet the growing needs of our populations means enhancing the quality of care and improving the organization of healthcare services for greater efficiency. In this context, the care pathway and digital health tools are at the heart of our reflections to support the rationalization of care and the improvement of our healthcare system. The PEPR Santé Numérique program, part of the France 2030 digital health acceleration strategy, has funded the SAFEPAW project, Societal Assets For E-healthcare Patient pAthWays.
The aim of the SAFEPAW project (a PEPR Digital Health project) is to assess the potential impact of methods for analyzing and optimizing patient care pathways, from an economic and organizational point of view, while enabling algorithms to be modulated according to patient characteristics (PREMS/PROMS, socio-economic characteristics, frailty) and from 3 different points of view (regulator, healthcare professionals and patients).
The SAFEPAW project, coordinated by Carine Milcent (PSE), is led by a multidisciplinary consortium of 6 partners (health economists, biostatisticians, computer scientists, logisticians, legal experts).
Thesis on the Care Pathway The aim of the work will be to describe the care pathways of people with mental illness, in particular children with attention deficit hyperactivity disorder (ADHD), in relation to various characteristics of the children, their families and the care available, and to compare them with a theoretical pathway. The work will be carried out using different data sources.
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