Big data redefines the traditional scientific methods used in medicine
Healthcare professionals are applying big data and analytics to clinical challenges. This is just the beginning of a redefinition in the traditional scientific methods used in medicine.
Stanford University will host a big data in biomedicine conference May 20-22, 2015 for medical researchers hailing from colleges and universities, hospitals, government, and industry. The goals are to encourage collaboration, address challenges, and identify actionable steps for harnessing big data in healthcare.
There are plenty of incentives. Whether through mega-scientific computing projects that process petabytes of data or through more informal ways of looking at data and analyzing it in new ways to reach outcomes that were previously unattainable, medicine is marching forward in applying big data and analytics to clinical challenges.
For instance, at Lucile Packard Children's Hospital Stanford in 2011, a young girl from Reno, Nevada, was flown by helicopter to the hospital, where she was admitted to the intensive care unit. The girl had lupus, which attacks the body's healthy tissues and can cause permanent kidney damage. An interdisciplinary team of doctors had to weigh the risks of using a coagulant that could thin blood and help prevent clots against the counter risks of complicating surgery, causing a stroke or creating a bleed into an organ. The team needed data.
A young physician named Jennifer Frankovich resorted to using a database of children with lupus that she had been helping to build. Part of the database work had entailed digitalizing charts and making data searchable with keywords. Through database searches, Dr. Frankovich was able to look at every pediatric lupus patient who had come through the hospital to see how many of them developed blood clots, and what the risk factors were. From there, she could calculate whether the risks of a blood clot in her current patient justified the risks of prescribing an anti-coagulant. The calculations indicated that the risk was worth taking, and the patient was given an anti-coagulant. The patient immediately showed signs of improvement.
Atul Butte, an entrepreneur and associate professor of pediatrics at the Stanford School of Medicine, compared Dr. Frankovich's work to a "seismic shift" happening in medicine. "The idea here is, the scientific method itself is growing obsolete," Butte said.
This scientific method as it has existed for decades and continues to exist in medicine today consists of a team of eminently qualified specialists from a variety of medical fields consulting with each other and sharing their collective experiences of treatment options and outcomes for the patient. In cases where unusual circumstances or risks present themselves, medical literature and empirical evidence that the scientific method demands are often missing. This happened in the Stanford lupus case, and this is where Dr. Frankovich was able to fill in the blanks with insights from data.
Is this the end of the story? Not quite.
Administrators at that hospital still feel it is safer to trust the wisdom of a team of doctors in urgent cases than to search medical records for data about what's worked in the past. In a January 2015 interview with NPR, Dr. Frankovich agreed, noting that "analyzing data is complicated and requires specific expertise. What if the search engine has bugs, or the records are transcribed incorrectly? There's just too much room for error....It's going to take a system to interpret the data, and that's what we don't have yet."
This comes full circle to the upcoming big data conference at Stanford this spring. The conference announcement states, "While other industries have been far more successful at harnessing the value from large-scale integration and analysis of big data, health care is just getting its feet wet. Yes, providers and payers are increasingly investing in their analytical capabilities to help them make better sense of the changing health care environment, but it is still early days."
True enough, but in healthcare settings like the Cleveland Clinic, doctors and medical practitioners are already making use of big data and analytics that diagnose conditions and prescribe treatments. What the analytics say is simply entered into the discussions that interdisciplinary teams of doctors have when they review patients. And while data quality and integration issues in healthcare continue to persist, there is an unmistakable beginning of a redefinition in the traditional scientific methods used in medicine.