Mapping the Flow of Pediatric Trauma Patients Using Process Mining.

Durojaiye AB1,2, McGeorge NM2, Puett LL3, Stewart D4, Fackler JC5, Hoonakker PLT6, Lehmann HP1, Gurses AP1,2,7,8,9.

Author information

1: Division of Health Sciences Informatics, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States.
2: Armstrong Institute Center for Health Care Human Factors, Johns Hopkins Medicine, Baltimore, Maryland, United States.
3: Department of Pediatric Nursing, Johns Hopkins Hospital, Baltimore, Maryland, United States.
4: Department of Pediatric Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States.
5: Division of Pediatric Anesthesiology and Critical Care Medicine, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States.
6: Center for Quality and Productivity Improvement, University of Wisconsin, Madison, Wisconsin, United States.
7: Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University, Baltimore, Maryland, United States.
8: Malone Center for Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States.
9: Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States.

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.

Georg Thieme Verlag KG Stuttgart · New York.