Mapping care pathways in children with TBI using Markov Models.

Brain injuries come with a significant societal burden. Focus should be brought not only to prevention but also towards improved quality of care. Currently, there is too much unexplainable variation in care trajectories. One category of patients less studied in this domain is pediatric traumatic brain injuries. The reason for this is lack of sufficient large numbers. To overcome this problem, administrative data holds the potential to provide a solution. By using Markov chain models, we can identify the different care pathways that patients with traumatic brain injuries take. To analyze the data, first the care pathways are clustered based on their similarity. This allowed for identification of different groups of patients who received similar care. Then, the pathways were reconstructed to form an expected care pathway for each cluster, which enabled interpretation of the care received. This study provides a novel technique for the mapping of causal care pathways for patients with traumatic brain injuries using administrative data. By identifying different care pathways, we can better understand the variation in care and work towards improving the quality of care received by patients. This is particularly important for children with traumatic brain injuries, who may have unique care needs that require specific attention.