Predictive analytics works particularly well for this type of patient identification, Courtright says, because it isn’t reliant on a clinician’s witness of warning signs.
“We know one of the barriers to getting these services to seriously ill patients, particularly in a hospital setting, is the focus in hospitals on the acute problem,” says Courtright. “When clinicians are so busy, they’re focused on what the patient came in from. It’s hard to step back and see the whole person as a trajectory.”
Palliative Connect initially ran as a pilot program at one of Penn Medicine’s hospitals from December 2017 to February 2018. The program assisted in identifying 85 patients for consultation, compared to 22 that would have been identified in a similar patient population without predictive analytics — a 74 percent increase. In July, Courtright published the full results of the pilot program in the Journal of General Medicine.
Since the inception of Palliative Connect, Penn Medicine has expanded its use to increase the reach of expert palliative care for the seriously ill. Researchers have also begun conducting a second pilot program at another one of the system’s hospitals, this time with an increased number of patient participants.
READ MORE: Discover how predictive analytics can play an influential role in operating rooms.
Predictive Analytics Makes Headway in Imaging
When it comes to medical imaging, predictive analytics is already making waves in speed and accuracy.
CheXNeXt, an artificial intelligence algorithm being trained and studied by researchers at Stanford University, is able to screen chest X-rays in a matter of seconds to detect 14 different pathologies with an accuracy rivaling that of radiologists. CheXNeXt researchers hope to be able to use the algorithm to help with the diagnosis of urgent care or emergency patients who come in with a cough.
Although the algorithm has yet to be introduced in a clinical setting, Dr. Matthew Lungren sees this technology changing the way care is offered by prioritizing patients based on predicted outcomes.
“The algorithm could triage the X-rays, sorting them into prioritized categories for doctors to review, like normal, abnormal or emergent,” says Lungren, assistant professor of radiology at the Stanford University Medical Center, in a Stanford Medicine article. “We need to be thinking about how far we can push these AI models to improve the lives of patients anywhere in the world.”