Artificial intelligence in trauma systems

Abstract

Local trauma care and regional trauma systems are data-rich environments that are amenable to machine learning, artificial intelligence, and big-data analysis mechanisms to improve timely access to care, to measure outcomes, and to improve quality of care. Pilot work has been done to demonstrate that these methods are useful to predict patient flow at individual centers, so that staffing models can be adapted to match workflow. Artificial intelligence has also been proven useful in the development of regional trauma systems as a tool to determine the optimal location of a new trauma center based on trauma-patient geospatial injury data and to minimize response times across the trauma network. Although the utility of artificial intelligence is apparent and proven in small pilot studies, its operationalization across the broader trauma system and trauma surgery space has been slow because of cost, stakeholder buy-in, and lack of expertise or knowledge of its utility. Nevertheless, as new trauma centers or systems are developed, or existing centers are retooled, machine learning and sophisticated analytics are likely to be important components to help facilitate decision-making in a wide range of areas, from determining bedside nursing and provider ratios to determining where to locate new trauma centers or emergency medical services teams.