ORAHS2024
Abstract Submission

119. Clinical pathways optimization of long-term and chronic patients in Quebec by Process Mining Approach

Contributed abstract in session MC-3: Evaluation and Implementation, stream Regular talks.

Monday, 11:00-12:30
Room: Room S2

Authors (first author is the speaker)

1. Luca Murazzano
Département d'Opérations et systèmes de décision, Université Laval
2. Paolo Landa
Département d’opérations et systèmes de décision, Université Laval
3. Jean-Baptiste Gartner
Management, Universite Laval
4. Andre Cote
Management, Universite Laval
5. Mohamed-Hakim Raki
FSA-Management, Université Laval

Abstract

Quebec has an increasingly aging population with a growing number of long-term and chronic conditions. Within these chronic conditions, pulmonary and respiratory diseases impact 8% of the overall Quebec population. It is important to meet the service demand effectively and efficiently for this population, analyzing the existing care processes and ensuring the right service configuration. The objective of this study consists of understanding and optimizing the organizational costs and performance of the treatments provided to the patients of the Cardiology and Respiratory University Hospital in Quebec, using a process mining approach. The application of this method will identify how the hospital organization can best deploy resources to meet the needs of the patients. Hospital data from 2018 to 2022 was collected from four macro clinical activities: Emergency Department, Ambulatory, Hospitalisation, and Medical Imaging. Data were processed through a subsequent cleaning and refining process to obtain the raw clinical pathways of the patients. Then a subsequent pass through a process mining tool enabled the identification of macro and micro trajectories for the different disease types. The results made it possible to identify critical points within the clinical pathways and where action is needed to make the care pathway more efficient.

Keywords

Status: accepted

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