Health Services and Performance Research Lab (EA 7425), Lyon 1 Claude Bernard University, Lyon, France;
Decision and Information Systems for Production systems (EA 4570), INSA Lyon, UJM-Saint Étienne, France;
Health Data Department, Lyon University Hospital, Lyon, France;
Sciences – Health Interdisciplinary Doctoral School (ED 205), Lyon 1 Claude Bernard University, Lyon, France
Research Axis: Axis 1 : The performance of health care services
Thesis title: Global method for predicting the length of stay in hospital using incremental and evolutionary data
Estimating the length of hospital stay has to be done for every admission to properly plan care activities. An inaccurate estimation can make the organization inefficient, cause work overload for healthcare professionals and a longer waiting time for patients. Hospitals will have to optimize resources use to keep up with the increasing need for healthcare and the budgets restrictions. In this context, the thesis theme is to develop a new length of stay prediction method that will help the hospital bed manager to accurately estimate the length of stays at the time of admission and during the whole hospital stay.
Year of registration: 2019
Holder of a general engineering degree from ESIGELEC specializing in Big Data and Digital Transformation, Vincent Lequertier is a doctoral student in epidemiology, public health and health services research. His interests are centered around Artificial Intelligence and data representation.