Structure de représentation de données pour l'optimisation de la prise de décision clinique à l’unité des soins intensifs pédiatriques

Abstract

Clinical decision-making in intensive care is a complex process that involves interpreting large amounts of data from various sources. It relies on knowledge bases and scientific recommendations to make informed decisions. However, the representation of clinical data is crucial for clinicians to interpret the information quickly and efficiently. To aid in this process, clinical decision support systems (CDSS) are increasingly used to improve patient outcomes. We conducted a study at Sainte-Justine Hospital's pediatric intensive care unit (PICU) to optimize the representation of clinical data by creating a data structure that supports clinicians in their cognitive process and facilitates the integration of CDSS based on clinical needs and workflow. We first observed clinical activities in the PICU to better understand the workflow. We then interviewed 11 participants from different staff categories, including intensivists, fellows, nurses, and nurse practitioners, to collect their decision support needs. Based on these discussions, we structured the data to design a prototype that illustrates the proposed representation. We held design meetings with 5 participants to present, revise, and adapt the prototype to meet their needs. As a result, we developed a 3-level data representation structure that prioritizes patients, assesses their conditions, and monitors their course in response to the clinicians' needs. However, further work is required to define and model the concepts of criticality, recognizing and evolving problems.

Date	17 Jul 2023
Original language	French
Awarding Institution	École de technologie supérieure
Supervisor	Rita Noumeir (Supervisor) & Philippe Jouvet (Co-supervisor)