Assessing trauma care provider judgement in the prediction of need for life-saving interventions

Injury. 2015 May;46(5):791-7. doi: 10.1016/j.injury.2014.10.063. Epub 2014 Nov 18.


Introduction: Human judgement on the need for life-saving interventions (LSI) in trauma is poorly studied, especially during initial casualty management. We prospectively examined early clinical judgement and compared clinical experts' predictions of LSI to their later occurrence.

Patients and methods: Within 10-15 min of direct trauma admission, we surveyed the predictions of pre-hospital care providers (PHP, 92% paramedics), trauma centre nurses (RN), and attending or fellow trauma physicians (MD) on the need for LSI. The actual outcomes including fluid bolus, intubation, transfusion (<1h and 1-6h), and emergent surgical interventions were observed. Cohen's kappa statistic (K) and percentage agreement were used to measure agreement among provider responses. Sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) were calculated to compare clinical judgement to actual patient interventions.

Results: Among 325 eligible trauma patient admissions, 209 clinical judgement of LSIs were obtained from all three providers. Cohen's kappa statistic for agreement between pairs of provider groups demonstrated no "disagreement" (K<0) between groups, "fair" agreement for fluid bolus (K=0.12-0.19) and blood transfusion 0-6h (K=0.22-0.39), and "moderate" (K=0.45-0.49) agreement between PHP and RN regarding intubation and surgical interventions, but no "excellent" (K ≥ 0.81) agreement between any pair of provider groups for any intervention. The percentage agreement across the different clinician groups ranged from 50% to 83%. NPV was 90-99% across providers for all interventions except fluid bolus.

Conclusions: Expert clinical judgement provides a benchmark for the prediction of major LSI use in unstable trauma patients. No excellent agreement exists across providers on LSI predictions. It is possible that quality improvement measures and computer modelling-based decision-support could reduce errors of LSI commission and omission found in resuscitation at major trauma centres and enhance decision-making in austere trauma settings by less well-trained providers than those surveyed.

Keywords: Clinical judgement; Human factors; Performance measures; Trauma care decision-assist; Trauma resuscitation.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Adult
  • Blood Transfusion* / statistics & numerical data
  • Decision Making
  • Emergency Medical Services* / methods
  • Female
  • Health Services Needs and Demand
  • Humans
  • Injury Severity Score
  • Male
  • Pilot Projects
  • Prospective Studies
  • Quality Improvement
  • Resuscitation*
  • Time Factors
  • Transportation of Patients
  • Trauma Centers / organization & administration
  • Trauma Centers / statistics & numerical data*
  • Wounds and Injuries / mortality
  • Wounds and Injuries / therapy*