Mapping the Flow of Pediatric Trauma Patients Using Process Mining

Durojaiye, Ashimiyu B.; McGeorge, Nicolette M.; Puett, Lisa; Stewart, Dylan; Fackler, James C.; Hoonakker, Peter; Lehmann, Harold P.; Gürses, Ayşe P.

doi:10.1055/s-0038-1668089

Cited by 29 publications

(22 citation statements)

References 32 publications

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“…The study applies the Flexible Heuristic Miner [20] algorithm to model data extracted from the trauma registry of a Level 1 pediatric trauma centre. Similar to our study, Durojaiye et al [10] describes the complexity of discovered models (28 discovered pathways) despite there being only 8 activities in the model. We note that application of the Flexible Heuristic Miner to data in our study generated extremely complex, unreadable models.…”

Section: Related Worksupporting

confidence: 83%

“…Suriadi et al [19] and Partington et al [2] describe approaches to performing comparative analysis using process mining techniques in a healthcare setting, i.e., four Australian hospitals treating patients with chest pain. Durojaiye et al [10] reports on the application of process mining to mapping the in-hospital flow of pediatric trauma patients with the aim of identifying major patient pathways. The study applies the Flexible Heuristic Miner [20] algorithm to model data extracted from the trauma registry of a Level 1 pediatric trauma centre.…”

Section: Related Workmentioning

confidence: 99%

“…There are several novel and challenging aspects to this study. Firstly, while process mining has been applied to patient flows and care pathways in emergency departments [9,10], to this point, process mining has not been applied to model the retrieval and transport phases of pre-hospital care. Secondly, the retrieval and transport phases include some events with short durations and rapid transitions interspersed with longer duration events.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Process Mining Analysis of Road Trauma Patient Pathways

Andrews

Wynn

Vallmuur

et al. 2020

IJERPH

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In this paper we report on key findings and lessons from a process mining case study conducted to analyse transport pathways discovered across the time-critical phase of pre-hospital care for persons involved in road traffic crashes in Queensland (Australia). In this study, a case is defined as being an individual patient’s journey from roadside to definitive care. We describe challenges in constructing an event log from source data provided by emergency services and hospitals, including record linkage (no standard patient identifier), and constructing a unified view of response, retrieval, transport and pre-hospital care from interleaving processes of the individual service providers. We analyse three separate cohorts of patients according to their degree of interaction with Queensland Health’s hospital system (C1: no transport required, C2: transported but no Queensland Health hospital, C3: transported and hospitalisation). Variant analysis and subsequent process modelling show high levels of variance in each cohort resulting from a combination of data collection, data linkage and actual differences in process execution. For Cohort 3, automated process modelling generated ’spaghetti’ models. Expert-guided editing resulted in readable models with acceptable fitness, which were used for process analysis. We also conduct a comparative performance analysis of transport segment based on hospital ‘remoteness’. With regard to the field of process mining, we reach various conclusions including (i) in a complex domain, the current crop of automated process algorithms do not generate readable models, however, (ii) such models provide a starting point for expert-guided editing of models (where the tool allows) which can yield models that have acceptable quality and are readable by domain experts, (iii) process improvement opportunities were largely suggested by domain experts (after reviewing analysis results) rather than being directly derived by process mining tools, meaning that the field needs to become more prescriptive (automated derivation of improvement opportunities).

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Section: Related Worksupporting

confidence: 83%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Comparative Process Mining Analysis of Road Trauma Patient Pathways

Andrews

Wynn

Vallmuur

et al. 2020

IJERPH

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“…3 Traumatically injured children generally experience many transitions of care and are treated by multiple clinicians including physicians from different services. 4,5 We previously identified 53 roles involved in the pediatric trauma care process at a children's hospital. 5,6 These roles include different groups of physicians that may not be involved in every transition but still require a clear assessment of the patient's hospital course, including a list of the patient's injuries, treatments, current status, and relevant past medical history.…”

Section: Context Of Pediatric Traumamentioning

confidence: 99%

Physician Perceptions of the Electronic Problem List in Pediatric Trauma Care

et al. 2019

Self Cite

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Objective To describe physician perceptions of the potential goals, characteristics, and content of the electronic problem list (PL) in pediatric trauma. Methods We conducted 12 semistructured interviews with physicians involved in the pediatric trauma care process, including residents, fellows, and attendings from four services: emergency medicine, surgery, anesthesia, and pediatric critical care. Using qualitative content analysis, we identified PL goals, characteristics, and patient-related information from these interviews and the hospital's PL etiquette document of guideline. Results We identified five goals of the PL (to document the patient's problems, to make sense of the patient's problems, to make decisions about the care plan, to know who is involved in the patient's care, and to communicate with others), seven characteristics of the PL (completeness, efficiency, accessibility, multiple users, organized, created before arrival, and representing uncertainty), and 22 patient-related information elements (e.g., injuries, vitals). Physicians' suggested criteria for a PL varied across services with respect to goals, characteristics, and patient-related information. Conclusion Physicians involved in pediatric trauma care described the electronic PL as ideally more than a list of a patient's medical diagnoses and injuries. The information elements mentioned are typically found in other parts of the patient's electronic record besides the PL, such as past medical history and labs. Future work is needed to evaluate the optimal design of the PL so that users with emergent cases, such as pediatric trauma, have access to key information related to the patient's immediate problems.

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“…Pediatric trauma care is multidisciplinary involving various healthcare professionals (HCPs) that coordinate across time and care location 4 , 5 . Patients arriving at the emergency departments (ED) of trauma centers are met by multidisciplinary trauma teams that provide life-saving resuscitation, stabilization, and definitive treatment.…”

Section: Background and Significancementioning

confidence: 99%

Evaluation of multidisciplinary collaboration in pediatric trauma care using EHR data

Durojaiye

Levin

Toerper

et al. 2019

Journal of the American Medical Informatics Association

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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.

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Mapping the Flow of Pediatric Trauma Patients Using Process Mining

Abstract: 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.

Cited by 29 publications

References 32 publications

A Comparative Process Mining Analysis of Road Trauma Patient Pathways

A Comparative Process Mining Analysis of Road Trauma Patient Pathways

Physician Perceptions of the Electronic Problem List in Pediatric Trauma Care

Evaluation of multidisciplinary collaboration in pediatric trauma care using EHR data

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