Mapping the Flow of Pediatric Trauma Patients Using Process Mining

Abstract

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.

Fig. 1

Overview of the methodology used in this study. The process mining technique was applied to the admission, discharge, and transfer data. The resulting data were merged with the patient encounter characteristic data to provide clinical context for analysis.

Appendix A

Process map.

Fig. 2

Study flow diagram of included encounters and reasons for excluded encounters. ED, emergency department; OR, operating room.

Fig. 3

The process map (fitness = 0.897) of the cohort showing the flow of patients from arrival to discharge. The numbers in the boxes are the total number of admissions in that inpatient location. ED, emergency department (pediatric ED in this study); ICU, intensive care unit (pediatric ICU in this study); OR, operating room; PACU, postanesthesia care unit.

Fig. 4

The process map for the Alpha encounter (top, fitness value = 0.975) and Bravo encounter (bottom, fitness value = 0.886) cohort showing the flow of patients from arrival to discharge. The numbers in the boxes are the total number of admissions in that inpatient location. ED, emergency department (pediatric ED in this study); ICU, intensive care unit (pediatric ICU in this study); OR, operating room; PACU, postanesthesia care unit.

Fig. 5

Screenshot showing the summary raw fitness cost statistics obtained from conformance checking of Bravo encounters against the Bravo process map. Max., maximum; Min., minimum; Std; standard deviation.