To accomplish this, Seton is using the ICPA for Healthcare tool, which lets officials mine unstructured data using natural language processing and search technologies. The company says more than 80% of healthcare data is unstructured and consists of physician notes, registration forms, discharge summaries, echocardiograms and other medical documents.
When combined with structured data, all this can paint a more accurate picture of trends, patterns and deviations, allowing clinicians to make better treatment decisions. She says this data was found in the history and physical part of the medical record in a narrative section.
The information gained from the unstructured data, however, is only as good as the user-created linguistic models using ICPA, Seton says. The accuracy of the linguistic and predictive models themselves depends on how well the model has been optimized by the user.
In terms of ROI, "The cost and angst to get all this information we need in structured data fields is pretty tremendous," says Froehlich. "This has the potential to reduce the need to provide a data field for every possible piece of data and of making everyone type everything into every single field ... which drives people up a wall. Intuitively we believe there is a cost benefit."
Seton Healthcare senior epidemiologist Christine Jesser concurs. "Highly skilled clinicians are spending inordinate amounts of time entering data into structured fields and this can reduce the time and effort it takes." While clinicians were already aware that smoking is an important consideration when looking at someone with CHF, she says the tool came back with some results they didn't expect for predicting the probability of a person's readmission to the hospital.
"The interesting things we found were their living status and were they in assisted living situations, and whether they had drug and alcohol abuse,'' Jesser says. "Those were some social factors that were only found in unstructured data that emerged as important predictors in the model."
If ICPA turns the data into information they can act on, "we get lower costs and a much faster way to get to information we provide,'' says Froehlich. "That's where our big hope is ... improving care without spending as much."
"We're at the cusp of a whole new way of [looking at] medical research,'' says UOIT's McGregor. "Data is multidimensional ... we have new types of data in just the neonatal setting we're looking to collect,'' such as brain activity and drugs being infused. Beyond looking at infection and apnea in preemies, they're also starting to look at other conditions, like hemorrhage of the brain in babies and adults. "We're targeting the most life-threatening conditions ... where we can make a significant difference."
IDC's Feldman acknowledges that "the technology itself is never going to be perfect... but computers, unlike people, are consistent." People can make judgments, though, and computers can't, so "if you combine the two, the outcome is more powerful" than relying on one or the other alone. Computers can "boost a physician's understanding of a patient" by crunching through more information than a human possibly could, and by then finding patterns in that information.
She sees predictive analytics and structured data making serious inroads within health care in the next five years, which will result in reduced costs and fewer adverse situations. The ability to use information better, says Feldman, "will substantially alter the future of health care."
Esther Shein is a freelance writer and editor. She can be reached at email@example.com .
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This story, "Data analytics driving medical breakthroughs" was originally published by Computerworld.