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Mapping the Flow of Pediatric Trauma Patients Using Process Mining
Ashimiyu B Durojaiye1,2, Nicolette M McGeorge2, Lisa L Puett3
1Division of Health Sciences Informatics, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States.
Insights
Process mining effectively mapped pediatric trauma patient flow, revealing key pathways and transitions. This analysis identified areas for improving patient flow and pediatric intensive care unit (PICU) resource utilization.
Area of Science:
- Medical Informatics
- Healthcare Operations Research
- Pediatric Trauma Care
Background:
- Inhospital pediatric trauma care involves multiple locations, impacting resource use.
- Understanding patient flow is crucial for optimizing care and resource allocation.
Purpose of the Study:
- To apply process mining for mapping pediatric trauma patient flow.
- To identify major patient pathways and care transitions.
- To pinpoint opportunities for improving patient flow and triage.
Main Methods:
- Utilized trauma registry data from a level I pediatric trauma center.
- Generated an event log from admission, discharge, and transfer data.
- Employed the Flexible Heuristics Miner algorithm to create process maps and assess conformance.
Main Results:
- Developed process maps for the overall cohort and specific trauma activation levels (Alpha and Bravo).
- Identified 28 patient pathways and 20 care transitions, with top pathways and transitions covering over 90% of cases.
- Found a significant number of pediatric intensive care unit (PICU) discharges to home without home care services.
Conclusions:
- Process mining successfully mapped pediatric trauma patient journeys using registry data.
- The study identified opportunities for enhancing trauma triage and optimizing PICU resource use.
Background:
Inhospital pediatric trauma care typically spans multiple locations, which influences the use of resources, that could be improved by gaining a better understanding of the inhospital flow of patients and identifying opportunities for improvement.
Objectives:
To describe a process mining approach for mapping the inhospital flow of pediatric trauma patients, to identify and characterize the major patient pathways and care transitions, and to identify opportunities for patient flow and triage improvement.
Methods:
From the trauma registry of a level I pediatric trauma center, data were extracted regarding the two highest trauma activation levels, Alpha (n = 228) and Bravo (n = 1,713). An event log was generated from the admission, discharge, and transfer data from which patient pathways and care transitions were identified and described. The Flexible Heuristics Miner algorithm was used to generate a process map for the cohort, and separate process maps for Alpha and Bravo encounters, which were assessed for conformance when fitness value was less than 0.950, with the identification and comparison of conforming and nonconforming encounters.
Results:
The process map for the cohort was similar to a validated process map derived through qualitative methods. The process map for Bravo encounters had a relatively low fitness of 0.887, and 96 (5.6%) encounters were identified as nonconforming with characteristics comparable to Alpha encounters. In total, 28 patient pathways and 20 care transitions were identified. The top five patient pathways were traversed by 92.1% of patients, whereas the top five care transitions accounted for 87.5% of all care transitions. A larger-than-expected number of discharges from the pediatric intensive care unit (PICU) were identified, with 84.2% involving discharge to home without the need for home care services.
Conclusion:
Process mining was successfully applied to derive process maps from trauma registry data and to identify opportunities for trauma triage improvement and optimization of PICU use.
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