Paging Dr. Data
For doctors who treat trauma patients, prediction is key: Will a patient die in the next 30 minutes? Why or why not? What about the next six hours? And after that? What’s the best treatment? How might the patient respond to that treatment in the best- and worst-case scenarios? Then what? These are mortal questions: Trauma kills more people between the ages of 1 and 44 than any other cause. Decisions made in those first few minutes and hours post-injury have the potential to save lives and speed recovery. Clinicians make critical judgments informed by hard-earned experience, best practice guidelines, and intuition. But those decisions are fraught with uncertainty.
This is where Alan Hubbard comes in. Hubbard, an associate professor of biostatistics at the School of Public Health, has been collaborating with Dr. Mitchell Cohen, a trauma surgeon at San Francisco General Hospital, to develop a predictive computer model for the prognosis of trauma patients. The model will predict the answer to one question—will the patient live or die?—but it could go much deeper. Clinicians might also consult it to determine the likely outcomes for different treatment options.
Read more about modeling doctors’ intuition here.
Image credit: Ryan Somma / flickr