Leveraging AI in Trauma Care: Insights for Practitioners
Artificial Intelligence (AI) is rapidly transforming various fields, and trauma care is no exception. The research article titled Science fiction or clinical reality: a review of the applications of artificial intelligence along the continuum of trauma care provides a comprehensive overview of how AI can be applied to enhance trauma care from injury prediction to patient outcomes. This blog aims to distill the key findings from the research and offer actionable insights for practitioners to improve their skills or encourage further research.
Injury Prediction
AI can refine injury prediction, particularly for motor vehicle crashes (MVCs). Studies have shown that AI algorithms can predict crash severity with an accuracy ranging from 0-96%, depending on the input variables and the complexity of the algorithm used. For instance, Elamrani Abou Assad et al. achieved prediction accuracies of 92.00% for a Support Vector Machine (SVM) and 93.34% for a Multilayer Perceptron (MLP).
These predictive models can help first responders and hospitals prepare for the likelihood of injury severity, thus optimizing emergency responses. Practitioners can leverage these models to improve pre-hospital care and resource allocation.
Pre-Hospital Triage
AI can also assist in triaging patients before they arrive at the hospital. Remote triage systems can predict the need for critical care or life-saving interventions, helping EMS teams make informed decisions about hospital selection and resource allocation. For example, Liu et al. developed an MLP that used vitals, demographics, and Glasgow Coma Scale (GCS) to determine the need for life-saving interventions with an AUC of 0.99.
Practitioners should consider integrating AI-based remote triage systems to enhance the efficiency and accuracy of pre-hospital care.
Emergency Department Volumes
AI has been shown to predict trauma volumes within the emergency department (ED) using inputs like date, traffic, special events, and weather conditions. Studies by Stonko et al. and Dennis et al. demonstrated that AI could predict mean Injury Severity Score (ISS), total number of traumas, and number of penetrating traumas with a correlation of 0.87-0.89.
Better prediction of trauma volumes can improve resource allocation, leading to cost savings and better patient outcomes. Practitioners can use these predictive models to optimize staffing and resource management in the ED.
Initial Assessment
AI can support initial diagnostic and therapeutic decision-making through patient severity assessments. For instance, Bektas et al. compared a logistic regression with an ANN to supplement CT in detecting cervical spine injuries (CSI). The ANN had a significantly better negative predictive value than the logistic model at 97.3% versus 87.9%, respectively.
Practitioners should explore integrating AI-based tools for initial assessments to enhance diagnostic accuracy and decision-making.
Outcomes
AI can predict various patient outcomes, including complications, survival, and discharge disposition. For example, the Trauma Outcome Predictor (TOP) uses data such as demographics, vital signs, and injury characteristics to predict mortality and morbidity with c-statistics up to 0.941.
Practitioners can use AI-based outcome prediction tools to plan interventions, manage patients, and inform end-of-life discussions.
Conclusion
AI has a promising role within trauma surgery practice and is worth the time and investment needed to prove and establish its specific uses. Given the technical expertise required to design, evaluate, and validate these algorithms, this endeavor will require interdisciplinary collaboration between physicians, computer scientists, statisticians, and administrators. These tools have the promise of changing clinical practice and improving patient outcomes and population health.
To read the original research paper, please follow this link: Science fiction or clinical reality: a review of the applications of artificial intelligence along the continuum of trauma care.
