Are there ways to know which cancer patients are at risk for an ER visit?
What if doctors could look into a crystal ball and predict which of their patients might be at risk of getting sick enough to go to the emergency room? What if they could use that prediction to help patients get treatment more quickly, with less fear and uncertainty, and with a greater chance of returning home rather than being admitted to the hospital? For at least one group of patients, that’s exactly what researchers at Penn Medicine are trying to do. But instead of peering into a crystal ball, they’re attempting to harness the power of big data.
For this project, doctors and data miners are specifically focusing on lung cancer patients. By flagging things like recent lab tests, radiology visits, or patient-reported symptoms, Penn’s team is hoping to come up with a formula that will predict when a patient is likely to end up visiting the emergency room. Right now, the formula can predict an estimated one out of every three ER visits, giving doctors the chance to take action before a patient gets to that point.
“Once we get the alert, we can call the patient ourselves,” said Tracey Evans, MD, an associate professor of Clinical Medicine in Penn’s Abramson Cancer Center and one of the doctors piloting the program. “We can schedule them for a visit to our clinic. We can recommend more frequent follow-ups or increase the steps they are taking for home care. All of this stems from big data, and the hope is it can help keep patients out of the emergency room.”
Read full article: Can Big Data Help Cancer Patients Avoid ER Visits?
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