The Parkland Trauma Index of Mortality (PTIM) is a machine learning algorithm and the only known model that uses electronic medical record data to predict—every hour—48-hour mortality during the first 72 hours of hospitalization, thus evolving with the patient’s physiologic response to trauma. Over a one-year period, PTIM has correctly identified 89% of the high-risk trauma patients and 92% of the low-risk trauma patients.

This model was developed because provider teams needed a dynamic tool to estimate patient mortality risk for prioritization of interventions as the teams must make many consequential decisions about trauma care in rapid succession; i.e., when to stabilize versus intervene, how to sequence interventions, etc. In many cases, these decisions can feel like equal parts instinct, art, and science, leading to delays and disagreements among care teams.

PTIM has the potential to make an enormous impact on trauma centers to enhance patient care, especially in the first 72 hours, when many of the critical-condition patients (from motor vehicle accidents or other traumatic events) arrive at hospitals with no identification and little to no information about their past medical histories. Prior industry models provided only a static, one-time score at the time of admission.