CoChat
Sign Up
Cited by 32Electronic Health Records SystemsHospital Admissions and OutcomesTrauma and Emergency Care Studies

Evaluation of multidisciplinary collaboration in pediatric trauma care using EHR data

Open access
Durojaiye, Ashimiyu B, Levin, Scott, Toerper, Matthew, Kharrazi, Hadi, Lehmann, Harold P, Gurses, Ayse P·2019·Journal of the American Medical Informatics Association
DOI 10.1093/jamia/ocy184PMC PMC6515526
Open this paper in CoChat — free accountOpen PDF

Abstract

Objectives: The study sought to identify collaborative electronic health record (EHR) usage patterns for pediatric trauma patients and determine how the usage patterns are related to patient outcomes. Materials and Methods: A process mining–based network analysis was applied to EHR metadata and trauma registry data for a cohort of pediatric trauma patients with minor injuries at a Level I pediatric trauma center. The EHR metadata were processed into an event log that was segmented based on gaps in the temporal continuity of events. A usage pattern was constructed for each encounter by creating edges among functional roles that were captured within the same event log segment. These patterns were classified into groups using graph kernel and unsupervised spectral clustering methods. Demographics, clinical and network characteristics, and emergency department (ED) length of stay (LOS) of the groups were compared. Results: Three distinct usage patterns that differed by network density were discovered: fully connected (clique), partially connected, and disconnected (isolated). Compared with the fully connected pattern, encounters with the partially connected pattern had an adjusted median ED LOS that was significantly longer (242.6 [95% confidence interval, 236.9–246.0] minutes vs 295.2 [95% confidence, 289.2–297.8] minutes), more frequently seen among day shift and weekday arrivals, and involved otolaryngology, ophthalmology services, and child life specialists. Discussion: The clique-like usage pattern was associated with decreased ED LOS for the study cohort, suggesting greater degree of collaboration resulted in shorter stay. Conclusions: Further investigation to understand and address causal factors can lead to improvement in multidisciplinary collaboration.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/jamia/ocy184

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Studies using raw audit logs fit into three broad categories: manual mapping, identification of networks, application of automatic methods.

    “Studies using raw audit logs fit into three broad categories:”

    From Electronic health record activity changes around new decision support implementation: monitoring using audit logs and topic modeling (2025) · cites this paper as [82]

    The Materials and Methods sections describe constructing networks from EHR event logs and classifying those networks, but do not classify studies using raw audit logs into three categories. Searches for the proposed categories and for manual mapping found no passage stating that taxonomy; page numbers were not available in our copy. (LLM, conf=0.70)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

No references could be resolved yet.

Research works printed but not linked: 49

  1. [3]Seifert J Incidence and economic burden of injuries in the United States. J Epidemiol Community Health 2007; 61 (10): 926.
  2. [5]Durojaiye AB, McGeorge NM, Puett LL et al Mapping the flow of pediatric trauma patients using process mining. Appl Clin Inform 2018; 9 (3): 654– 66. 30134474 PMC6105335
  3. [6]Vernon DD, Furnival RA, Hansen KW et al Effect of a pediatric trauma response team on emergency department treatment time and mortality of pediatric trauma victims. Pediatrics 1999; 103 (1): 20– 24. 9917434
  4. [7]Perno JF, Schunk JE, Hansen KW et al Significant reduction in delayed diagnosis of injury with implementation of a pediatric trauma service. Pediatr Emerg Care 2005; 21 (6): 367– 71. 15942513
  5. [8]Bach JA, Leskovan JJ, Scharschmidt T et al The right team at the right time - Multidisciplinary approach to multi-trauma patient with orthopedic injuries. Int J Crit Illn Inj Sci 2017; 7 (1): 32– 7. 28382257 PMC5364767
  6. [9]Stinner DJ, Brooks SE, Fras AR et al Caring for the polytrauma patient: is your system surviving or thriving? Am J Orthop 2013; 42 (5): E33– 4. 23710484
  7. [10]Suk M We need better care coordination for polytraumatized patients. Am J Orthop 2013; 42 (7): 302. 24078939
  8. [11]Curtis K, Foster K, Mitchell R et al How is care provided for patients with paediatric trauma and their families in Australia? A mixed-method study. J Paediatr Child Health 2016; 52 (8): 832– 6. 27203550
  9. [12]Ameratunga S et al Children admitted to hospital following unintentional injury: perspectives of health service providers in Aotearoa/New Zealand. BMC Health Serv Res 2010; 10: 333. 21138584 PMC3016356
  10. [13]Bae S-H, Nikolaev A, Seo JY et al Health care provider social network analysis: a systematic review. Nurs Outlook 2015; 63 (5): 566– 84. 26162750
  11. [14]Chambers D, Wilson P, Thompson C et al Social network analysis in healthcare settings: a systematic scoping review. PLoS One 2012; 7 (8): e41911. 22870261 PMC3411695
  12. [15]Cunningham FC, Ranmuthugala G, Plumb J et al Health professional networks as a vector for improving healthcare quality and safety: a systematic review. BMJ Qual Saf 2012; 21 (3): 239– 49. PMC3285140 22129933
  13. [16]Tasselli S Social networks of professionals in health care organizations: a review. Med Care Res Rev 2014; 71 (6): 619– 60. 25380607
  14. [17]Brunson JC, Laubenbacher RC Applications of network analysis to routinely collected health care data: a systematic review. J Am Med Inform Assoc 2018; 25 (2): 210– 21. 29025116 PMC6664849
  15. [18]Soulakis ND, Carson MB, Lee YJ et al Visualizing collaborative electronic health record usage for hospitalized patients with heart failure. J Am Med Inform Assoc 2015; 22 (2): 299– 311. 25710558 PMC4394967
  16. [19]Chen Y, Patel MB, McNaughton CD, Malin BA Interaction patterns of trauma providers are associated with length of stay. J Am Med Inform Assoc 2018 Feb 22; 25(7): 790– 9. 29481625 PMC6016682
  17. [20]He S, Gurr G, Rea S et al Characterizing the structure of a patient’s care team through electronic encounter data analysis. Stud Health Technol Inform 2015; 216: 21– 5. 26262002
  18. [21]Gray JE, Davis DA, Pursley DM et al Network analysis of team structure in the neonatal intensive care unit. Pediatrics 2010; 125 (6): e1460– 7. 20457681
  19. [22]Gray JE, Feldman H, Reti S Using Digital Crumbs from an Electronic Health Record to identify, study and improve health care teams. AMIA Annu Symp Proc 2011; 2011: 491– 500. 22195103 PMC3243159
  20. [23]Chen Y, Lorenzi NM, Sandberg WS Identifying collaborative care teams through electronic medical record utilization patterns. J Am Med Inform Assoc 2017; 24 (e1): e111– 20. 27570217 PMC6080725
  21. [24]Carson MB, Scholtens DM, Frailey CN et al An outcome-weighted network model for characterizing collaboration. PLoS One 2016; 11 (10): e0163861. 27706199 PMC5051930
  22. [25]Conca T, Saint-Pierre C, Herskovic V et al Multidisciplinary collaboration in the treatment of patients with type 2 diabetes in primary care: analysis using process mining. J Med Internet Res 2018; 20 (4): e127. 29636315 PMC5915667
  23. [26]ATLS Subcommittee; American College of Surgeons’ Committee on Trauma; International ATLS Working Group. Advanced trauma life support (ATLS®): the ninth edition. J Trauma Acute Care Surg 2013; 74 (5): 1363– 6. 23609291
  24. [27]Avraham JB, Bhandari M, Frangos SG et al Epidemiology of paediatric trauma presenting to US emergency departments: 2006–2012. Inj Prev 2017 Oct 22 [E-pub ahead of print]. 29056586
  25. [28]Macy ML, Zonfrillo MR, Cook LJ et al Patient- and community-level sociodemographic characteristics associated with emergency department visits for childhood injury. J Pediatr 2015; 167 (3): 711– 8.e1–4. 26141551 PMC4554798
  26. [31]Durojaiye AB, Puett LL, Levin S et al Linking electronic health record and trauma registry data: assessing the value of probabilistic linkage. Methods Inf Med 2018 Nov 19. DOI: 10.1055/s-0038-1675220. [E-pub ahead of print]. 30453337 · DOI
  27. [32]van der Aalst W Process Mining: the missing link. InProcess mining 2016 (pp. 25–52). Springer, Berlin, Heidelberg.
  28. [33]van der Aalst W, 2016 In Process Mining (pp. 3–23). Springer, Berlin, Heidelberg
  29. [35]Rojas E, Munoz-Gama J, Sepúlveda M et al Process mining in healthcare: a literature review. J Biomed Inform 2016; 61: 224– 36. 27109932
  30. [36]Verbeek HM, Buijs JC, Van Dongen BF, van der Aalst WM Prom 6: The process mining toolkit. Proc. of BPM Demonstration Track. 2010; 615: 34– 9.
  31. [37]Van Der Aalst WM, Reijers HA, Song M Discovering social networks from event logs. Comput Support Coop Work 2005; 14 (6): 549– 93.
  32. [38]Ferreira DR, Alves C Discovering user communities in large event logs. In International Conference on Business Process Management. New York: Springer, 2011.
  33. [39]Benham-Hutchins MM, Effken JA Multi-professional patterns and methods of communication during patient handoffs. Int J Med Inform 2010; 79 (4): 252– 67. 20079686
  34. [40]Jenks GF The data model concept in statistical mapping. International Yearb Cartogr 1967; 7: 186– 90.
  35. [41]Dent, Borden D., Jeffrey S. Torguson, Thomas W. Hodler Cartography: Thematic map design. Vol. 5 Boston, MA:: WCB/McGraw-Hill, 1999.
  36. [42]Csardi G, Nepusz T The igraph software package for complex network research. Int J Complex Syst 2006; 1695 (5): 1– 9.
  37. [44]Shervashidze N, Vishwanathan SVN, Petri T et al Efficient graphlet kernels for large graph comparison. In: Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS); 2009: 488–495; Clearwater Beach, FL.
  38. [45]Sugiyama M, Ghisu ME, Llinares-López F et al graphkernels: R and Python packages for graph comparison. Bioinformatics 2018; 34 (3): 530– 2. 29028902 PMC5860361
  39. [46]Karatzoglou A, Smola A, Hornik K et al kernlab-an S4 package for kernel methods in R. J Stat Softw 2004; 11 (9): 1– 20.
  40. [47]Von Luxburg U A tutorial on spectral clustering. Stat Comput 2007; 17 (4): 395– 416.
  41. [48]Bholowalia P, Kumar A EBK-means: a clustering technique based on elbow method and k-means in WSN. Int J Computer Appl 2014; 105 (9): 17– 24.
  42. [50]Kamada T, Kawai S An algorithm for drawing general undirected graphs. Inf Process Lett 1989; 31 (1): 7– 15.
  43. [51]Vawdrey DK, Wilcox LG, Collins S et al Awareness of the care team in electronic health records. Appl Clin Inform 2011; 2 (4): 395– 405. 22574103 PMC3345520
  44. [52]Hanauer DA, Ramakrishnan N Modeling temporal relationships in large scale clinical associations. J Am Med Inform Assoc 2013; 20 (2): 332– 41. 23019240 PMC3638191
  45. [53]Schlechter JA, Avik AL, DeMello S Is there a role for a child life specialist during orthopedic cast room procedures? A prospective-randomized assessment. J Pediatr Orthop B 2017; 26 (6): 575– 9. 28857958
  46. [54]Martin HA The power of lidocaine, epinephrine, and tetracaine (LET) and a child life specialist when suturing lacerations in children. J Emerg Nurs 2017; 43 (2): 169– 70. 28372769
  47. [55]Gursky B, Kestler LP, Lewis M Psychosocial intervention on procedure-related distress in children being treated for laceration repair. J Dev Behav Pediatr 2010; 31 (3): 217– 22. 20375734
  48. [56]Rucker DW Using telephony data to facilitate discovery of clinical workflows. Appl Clin Inform 2017; 8 (2): 381– 95. 28421225 PMC6241743
  49. [57]Hribar MR, Read-Brown S, Reznick L et al Secondary use of EHR timestamp data: validation and application for workflow optimization. AMIA Annu Symp Proc 2015; 2015: 1909– 17. 26958290 PMC4765636

Cited by

Sorted by citations

Authors
Durojaiye, A. B., Levin, S., Toerper, M., Kharrazi, H., Lehmann, H. P., & Gurses, A. P.
Affiliations
  • Johns Hopkins University (US)
  • Division of Health Sciences Informatics, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
ORCID
Venue
Journal of the American Medical Informatics Association
Publisher
Oxford University Press (OUP)
Published
2019-03-19
Volume
26
Issue
6
Pages
506-515
ISSN
  • 1067-5027 (ISSN-L)
  • 1527-974X
DOI
10.1093/jamia/ocy184
PMC
PMC6515526
OpenAlex
W2924136575
Cited by
32
References
0
Citations by year
2026: 1 · 2025: 5 · 2024: 4 · 2023: 4 · 2022: 7 · 2021: 7 · 2020: 2 · 2019: 2
FWCI
3.47
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of the American Medical Informatics Association (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Funders
Agency for Healthcare Research and Quality (R01HS023837)
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Medicine › Emergency Medicine › Hospital Admissions and Outcomes
  • Health Sciences › Medicine › Emergency Medicine › Trauma and Emergency Care Studies
Keywords
pediatric trauma care, multidisciplinary collaboration, process mining, network analysis, emergency department length of stay, minor injuries, trauma registry, network density, otolaryngology, ophthalmology, child life specialists
MeSH terms
Metadata, Social Network Analysis, Child, Cooperative Behavior, Emergency Service, Hospital, Humans, Interprofessional Relations, Length of Stay, Patient Care Team, Pediatrics, Traumatology, Data Mining, Electronic Health Records

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review