Effect of a Checklist on Advanced Trauma Life Support Workflow Deviations during Trauma Resuscitations without Pre-Arrival Notification

Kelleher, Deirdre C.; Bose, R. P. Jagadeesh Chandra; Waterhouse, Lauren J.; Carter, Elizabeth A.; Burd, Randall S.

doi:10.1016/j.jamcollsurg.2013.11.021

Cited by 38 publications

(26 citation statements)

References 35 publications

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“…Three medical logs were coded from surveillance videos of trauma resuscitations: (1) The endotracheal intubation (breathing tube insertion). (2) The initial evaluation phase (i.e., primary survey) of the trauma resuscitation [21], where the team looks for immediately life-threatening injuries. (3) The head to toe examination phase (i.e., secondary survey) of the trauma resuscitation.…”

Section: Empirical Experimental Resultsmentioning

confidence: 99%

Medical Workflow Modeling Using Alignment-Guided State-Splitting HMM

Yang

Zhou

Chen

et al. 2017

2017 IEEE International Conference on Healthcare Informatics (ICHI)

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Process mining techniques have been used to discover and analyze workflows in various fields, ranging from business management to healthcare. Much of this research, however, has overlooked the potential of hidden Markov models (HMMs) for workflow discovery. We present a novel alignment-guided state-splitting HMM inference algorithm (AGSS) for discovering workflow models based on observed traces of process executions. We compared the AGSS to existing methods using four real-world medical workflow datasets and a more detailed case study on one of them. Our numerical results show that AGSS not only generates more accurate workflow models, but also better represents the underlying process. In addition, with trace alignment to guide state splitting, AGSS is significantly more efficient (by a factor of O(n)) than previous HMM inference algorithms. Our case study results show that our approach produces a more readable and accurate workflow model that existing algorithms. Comparing the discovered model to the hand-made expert model of the same process, we found three discrepancies. These three discrepancies were reconsidered by medical experts and used for enhancing the expert model.

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Section: Empirical Experimental Resultsmentioning

confidence: 99%

Medical Workflow Modeling Using Alignment-Guided State-Splitting HMM

Yang

Zhou

Chen

et al. 2017

2017 IEEE International Conference on Healthcare Informatics (ICHI)

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“…[9] Recent opinion and studies have clearly established the advantages of ATLS guidelines and multidisciplinary TT, which include better organization, improvement of clinical and non-clinical skills, adherence to guidelines, [10] and priority approaches, if correctly implemented. [11] Strong evidence of a positive effect on mortality and morbidity is still lacking, though a trend toward improvement has been shown. [12][13][14] At the time, in 2010, that the present hospital administration acknowledged the need for improvement, ATLS was not yet included in local guidelines.…”

Section: Discussionmentioning

confidence: 99%

Impact of ATLS Guidelines and Trauma Team Introduction an 24 Hours Mortality in Severe Trauma in a Busy Italian Metropolitan Hospital: a Case Control Study.

Magnone¹

2015

Ulus Travma Acil Cerrahi Derg

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“…Step 1 focused on facilitating the inclusion of evidence‐based practices for trauma management during measure development. We identified published adult trauma patient care checklists that have been applied to both simulated and live patient care events, as well as standards of trauma care, including ATLS . Subject matter experts (SMEs), four board‐certified emergency medicine physicians and one clinical nurse, reviewed the guidelines and assessment measures to identify items that 1) were appropriate for trauma resuscitations, 2) apply across all types and severity of trauma resuscitations (universal items), and 3) were indicated in certain clinical presentations but were not universally relevant (conditional items).…”

Section: Methodsmentioning

confidence: 99%

An Event‐based Approach to Measurement: Facilitating Observational Measurement in Highly Variable Clinical Settings

Fernandez

Rosenman

Brolliar

et al. 2019

AEM Education and Training

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Background: Translational research in medical education requires the ability to rigorously measure learner performance in actual clinical settings; however, current measurement systems cannot accommodate the variability inherent in many patient care environments. This is especially problematic in emergency medicine, where patients represent a wide spectrum of severity for a single clinical presentation. Our objective is to describe and implement EBAM, an event-based approach to measurement that can be applied to actual emergency medicine clinical events. Methods:We used a four-step event-based approach to create an emergency department trauma resuscitation patient care measure. We applied the measure to a database of 360 actual trauma resuscitations recorded in a Level I trauma center using trained raters. A subset (n = 50) of videos was independently rated in duplicate to determine inter-rater reliability. Descriptive analyses were performed to describe characteristics of resuscitation events and Cohen's kappa was used to calculate reliability. Results:The methodology created a metric containing both universal items that are applied to all trauma resuscitation events and conditional items that only apply in certain situations. For clinical trauma events, injury severity scores ranged from 1 to 75 with a mean (AESD) of 21 (AE15) and included both blunt (254/360; 74%) and penetrating (86/360; 25%) traumatic injuries, demonstrating the diverse nature of the clinical encounters. The mean (AESD) Cohen's kappa for patient care items was 0.7 (AE0.3).

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Effect of a Checklist on Advanced Trauma Life Support Workflow Deviations during Trauma Resuscitations without Pre-Arrival Notification

Cited by 38 publications

References 35 publications

Medical Workflow Modeling Using Alignment-Guided State-Splitting HMM

Medical Workflow Modeling Using Alignment-Guided State-Splitting HMM

Impact of ATLS Guidelines and Trauma Team Introduction an 24 Hours Mortality in Severe Trauma in a Busy Italian Metropolitan Hospital: a Case Control Study.

An Event‐based Approach to Measurement: Facilitating Observational Measurement in Highly Variable Clinical Settings

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