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.

Keywords

pediatric trauma - process mining - trauma registry - patient pathway - care transition - conformance checking

Protection of Human and Animal Subjects

This study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was reviewed by Johns Hopkins Medicine Institutional Review Board.

• References

• 1 Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. Web-based Injury Statistics Query and Reporting System (WISQARS). Fatal Injury Data. 2016. Available at: https://www.cdc.gov/injury/wisqars/fatal.html . Accessed March 1, 2018
• 2 Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. Web-based Injury Statistics Query and Reporting System (WISQARS). Nonfatal Injury Data. 2016. Available at: https://www.cdc.gov/injury/wisqars/nonfatal.html Accessed March 1, 2018
• 3 Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. National Action Plan for Child Injury Prevention. Atlanta, GA: CDC, NCIPC. 2012 . Available at: https://www.cdc.gov/safechild/pdf/national_action_plan_for_child_injury_prevention.pdf Accessed March 1, 2018
• 4 Committee on Pediatric Emergency Medicine, Council on Injury; Violence, and Poison Prevention, Section on Critical Care, Section on Orthopaedics, Section on Surgery, Section on Transport Medicine, Pediatric Trauma Society, and Society of Trauma Nurses Pediatric Committee. Management of pediatric trauma. Pediatrics 2016; 138 (02) e20161569
• 5 Sharar SR. The ongoing and worldwide challenge of pediatric trauma. Int J Crit Illn Inj Sci 2012; 2 (03) 111-113
• 6 McFadyen JG, Ramaiah R, Bhananker SM. Initial assessment and management of pediatric trauma patients. Int J Crit Illn Inj Sci 2012; 2 (03) 121-127
• 7 American Academy of Pediatrics Committee on Pediatric Emergency Medicine; American College of Emergency physicians Pediatric Emergency Medicine Committee; Emergency Nurses Association Pediatric Committee. Handoffs: transitions of care for children in the emergency department. Pediatrics 2016; 138 (05) e20162680
• 8 Evans DC, Andrusiek DL, Sobolev B. Process mapping of a regional trauma system. In: Hall R, ed. Patient Flow: Reducing Delay in Healthcare Delivery. Boston, MA: Springer; 2013: 311-332
• 9 Hall R, Belson D, Murali P, Dessouky M. Modeling patient flows through the health care system. In: Hall R. , ed. Patient Flow: Reducing Delay in Healthcare Delivery. Boston, MA: Springer; 2013: 3-42
• 10 Ruchholtz S, Lewan U, Debus F, Mand C, Siebert H, Kühne CA. TraumaNetzwerk DGU(®): optimizing patient flow and management. Injury 2014; 45 (Suppl. 03) S89-S92
• 11 Hipps D, Jameson S, Murty A. , et al. The effect of introducing a Trauma Network on patient flow, hospital finances and trainee operating. Injury 2015; 46 (02) 195-200
• 12 Oredsson S, Jonsson H, Rognes J. , et al. A systematic review of triage-related interventions to improve patient flow in emergency departments. Scand J Trauma Resusc Emerg Med 2011; 19: 43
• 13 Sayah A, Rogers L, Devarajan K, Kingsley-Rocker L, Lobon LF. Minimizing ED waiting times and improving patient flow and experience of care. Emerg Med Int 2014; 2014: 981472
• 14 Catchpole KR, Gangi A, Blocker RC. , et al. Flow disruptions in trauma care handoffs. J Surg Res 2013; 184 (01) 586-591
• 15 Kolker A. Interdependency of hospital departments and hospital-wide patient flows. In: Hall R. , ed. Patient Flow: Reducing Delay in Healthcare Delivery. Boston, MA: Springer; 2013: 43-63
• 16 Zakrison TL, Rosenbloom B, McFarlan A. , et al. Lost information during the handover of critically injured trauma patients: a mixed-methods study. BMJ Qual Saf 2016; 25 (12) 929-936
• 17 Mann NC, Mackenzie E, Teitelbaum SD, Wright D, Anderson C. Trauma system structure and viability in the current healthcare environment: a state-by-state assessment. J Trauma 2005; 58 (01) 136-147
• 18 American College of Surgeons Committee on Trauma. Resources for Optimal Care of the Injured Patient. Chicago, IL: American College of Surgeons; 2014
• 19 Maryland Institute for Emergency Medical Services Systems. Maryland State Trauma Registry Data Dictionary for Pediatric Patients. 2014 . Available at: https://www.miemss.org/home/Portals/0/Docs/OtherPDFs/Web-registry-data-dictionary-pediatric.pdf?ver=2016-03-10-140444-350. Accessed March 1, 2018
• 20 American College of Surgeons, Committee on Trauma. National Trauma Data Standard Data Dictionary, 2017. Admissions. Available at: https://www.facs.org/~/media/files/quality%20programs/trauma/ntdb/ntds/data%20dictionaries/ntdsdatadictionary2017admissions.ashx . Accessed August 16, 2017
• 21 van der Aalst W. Process Mining: The missing link. Heidelberg: Springer; 2016: 25-52
• 22 van der Aalst W. Process Mining: Data Science in Action. Heidelberg: Springer-Verlag; 2016
• 23 Rozinat A, van der Aalst WMP. Conformance checking of processes based on monitoring real behavior. Inf Syst 2008; 33: 64-95
• 24 Rozinat A, Van der Aalst WM. Conformance testing: Measuring the fit and appropriateness of event logs and process models. International Conference on Business Process Management. Heidelberg: Springer; 2005: 163-176
• 25 Lismont J, Janssens AS, Odnoletkova I, Vanden Broucke S, Caron F, Vanthienen J. A guide for the application of analytics on healthcare processes: a dynamic view on patient pathways. Comput Biol Med 2016; 77: 125-134
• 26 Liu J, Erdal S, Silvey SA. , et al. Toward a fully de-identified biomedical information warehouse. AMIA Annu Symp Proc 2009; 2009: 370-374
• 27 Verbeek H, Buijs J, Van Dongen B, van der Aalst WM. P. Prom 6: the process mining toolkit. In: Proceedings of BPM Demonstration Track. 2010 ;615:34–39
• 28 Paster F, Helm E. From IHE audit trails to XES event logs facilitating process mining. Stud Health Technol Inform 2015; 210: 40-44
• 29 Weijters A, Ribeiro J. Flexible heuristics miner (FHM). In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining CIDM. 2011:310–317
• 30 De Weerdt J, De Backer M, Vanthienen J, Baesens B. A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Inf Syst 2012; 37: 654-676
• 31 Weijters A, van der Aalst WMP, De Medeiros AA. Process mining with the heuristics miner-algorithm. Technische Universiteit Eindhoven. Tech Rep WP 2006; 166: 1-34
• 32 Buijs JC, Van Dongen BF, van der Aalst WM. P. On the role of fitness, precision, generalization and simplicity in process discovery. OTM 2012; 7565: 305-322
• 33 van der Aalst WMP. Process mining: discovering and improving Spaghetti and Lasagna processes. In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining CIDM. 2011 :1–7
• 34 Desel J, Juhás G. “What Is a Petri Net?” Informal Answers for the Informed Reader. In: Ehrig H, Padberg J, Juhás G, Rozenberg G. , eds. Unifying Petri Nets. Lecture Notes in Computer Science. Vol. 2128. Berlin, Heidelberg: Springer; 2001: 1-25
• 35 Adriansyah A, van Dongen BF, van der Aalst WM. Conformance checking using cost-based fitness analysis. In: Enterprise Distributed Object Computing Conference (EDOC), 15th IEEE International. IEEE; 2011 :55–64
• 36 van der Aalst W. MP, Adriansyah A, van Dongen B. Replaying history on process models for conformance checking and performance analysis. Wiley Interdiscip Rev Data Min Knowl Discov 2012; 2: 182-192
• 37 StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX: StataCorp LP;
• 38 Wooldridge A, Carayon P, Hoonakker P. , et al. Understanding Team Complexity in Pediatric Trauma Care [Abstract]. Paper presented at the Human Factors and Ergonomics in Healthcare, New Orleans, LA, March 5–8, 2017
• 39 Curtis K, Foster K, Mitchell R, Van C. How is care provided for patients with paediatric trauma and their families in Australia? A mixed-method study. J Paediatr Child Health 2016; 52 (08) 832-836
• 40 Carayon P, Schoofs Hundt A, Karsh BT. , et al. Work system design for patient safety: the SEIPS model. Qual Saf Health Care 2006; 15 (Suppl. 01) i50-i58
• 41 Samal L, Dykes PC, Greenberg J. , et al. The current capabilities of health information technology to support care transitions. AMIA Annu Symp Proc 2013; 2013: 1231
• 42 Institute of Medicine. Health IT and Patient Safety: Building Safer Systems for Better Care. Washington, DC: The National Academies Press; 2012
• 43 American College of Surgeons. National Trauma Data Bank 2016: Pediatric Annual Report. Available at: https://www.facs.org/~/media/files/quality%20programs/trauma/ntdb/ntdb%20pediatric%20annual%20report%202016.ashx. Accessed October 24, 2017
• 44 Newgard CD, Staudenmayer K, Hsia RY. , et al. The cost of overtriage: more than one-third of low-risk injured patients were taken to major trauma centers. Health Aff (Millwood) 2013; 32 (09) 1591-1599
• 45 Kouzminova N, Shatney C, Palm E, McCullough M, Sherck J. The efficacy of a two-tiered trauma activation system at a level I trauma center. J Trauma 2009; 67 (04) 829-833
• 46 Dowd MD, McAneney C, Lacher M, Ruddy RM. Maximizing the sensitivity and specificity of pediatric trauma team activation criteria. Acad Emerg Med 2000; 7 (10) 1119-1125
• 47 Drendel AL, Gray MP, Lerner EB. A systematic review of hospital trauma team activation criteria for children. Pediatr Emerg Care 2017;
• 48 Fenton SJ, Campbell SJ, Stevens AM, Zhang C, Presson AP, Lee JH. Preventable pediatric intensive care unit admissions over a 13-year period at a level 1 pediatric trauma center. J Pediatr Surg 2016; 51 (10) 1688-1692
• 49 Goldstein SD, Cerullo M, Noje C. , et al. Implementation of a Smartphone-based Decision Tool to Determine Level of Care for Pediatric Head Trauma [Abstract]. Paper presented at the 2017 4th Annual Meeting of the Pediatric Trauma Society, November 2–4, 2017, Charleston, SC
• 50 Suriadi S, Mans R, Wynn M, Partington A, Karnon J. Measuring patient flow variations: a cross-organisational process mining approach. In: Ouyang C, Jung JY. (eds) Asia Pacific Business Process Management. AP-BPM 2014. Lecture Notes in Business Information Processing, vol 181. Cham: Springer;
• 51 Partington A, Wynn M, Suriadi S, Ouyang C, Karnon J. Process mining for clinical processes: a comparative analysis of four Australian hospitals. ACM Trans Manag Inf Syst 2015; 5: 19
• 52 Chazard E, Beuscart R. Graphical representation of the comprehensive patient flow through the hospital. AMIA Annu Symp Proc 2007; 2007: 110-114
• 53 Kumar V, Park H, Basole RC, Braunstein M, Kahng M, Chau DH. , et al. Exploring clinical care processes using visual and data analytics: challenges and opportunities. In Proceedings of the 20th ACM SIGKDD conference on knowledge discovery and data mining workshop on data science for social good. 2014
• 54 van der Aalst WMP, de Leoni M, ter Hofstede AH. Process mining and visual analytics: Breathing life into business process models. BPM Center Report BPM-11-15, BPMcenter.org 17 2011:699–730