Clinical trials are great, if you have the time and resources to complete them.
It’s no secret that trials carry a high price tag, and can take years to finish. With current technology, there is an alternative. Can you guess the secret ingredient? Big data, of course!
With access to patient health records, doctors and scientists have the ability to analyze heaps of medical information from an extensive database. By comparing symptoms to similar cases, medical professionals are also able to more accurately diagnose health conditions.
A recent New York Times article proves this point flawlessly. Jennifer Frankovich, a rheumatologist at Stanford’s Packard Children’s Hospital, diagnosed a 13-year-old girl with symptoms suggestive of kidney failure with the autoimmune disease, lupus.
As a team of medical doctors rushed to help the young girl, Frankovich remembered past medical cases that identically mimicked her patient’s particular symptoms. From what she could recall, this patient had a likely chance of developing a life-threatening blood clot.
To prove this to herself and her colleagues, Frankovich did extensive research. While she could not find any medical literature on the subject, Frankovich went into the hospital’s database and looked at all the lupus patients that had been treated at Stanford’s Packard Children’s Hospital in the past five years. After calculating some statistical data, it was determined that the 13-year-old patient was indeed at risk of developing a blood clot, and she was administered the appropriate medicine.
While there is no way to prove whether the young lupus patient would have developed a clot without the drug, this research potentially saved her life. Had Frankovich not had access to this prior medical information, it would have taken an expensive and long clinical trial process to come to this conclusion.
A future clinical trial may one day prove Frankovich’s hypothesis true; but for now, big data is working in the short term to save patients’ lives.