Do insurers hold the key to predicting preventative health care and lowering costs?
When it comes to medical data, insurance companies have a comprehensive view and keep great records, giving insurers “the potential to be heroes.”
Insurers’ medical records of patients can be used in predictive analytics models to spot and treat health issues before they progress into more serious problems, according to a study published in the Journal of Biomedical Informatics.
In their study, the researchers used insurance claims from 2011 to 2013 to predict hospitalizations due to issues related to inflammatory bowel disease, and the initiation of biologics to treat the patients. The data was used to derive and optimize a predictive model from a 2011 set of 7,771 members, predicting their outcomes the following year. The best-performing model was then applied to a 2012 set of 7,450 members to predict their outcomes in 2013.
Related: Using Big Data to design better health care plans
The models predicted both IBD-related hospitalizations and the initiation of biologics, with average positive predictive values of 17 percent and 11 percent, respectively — each a 200 percent improvement over chance. The researchers then used topic modeling to identify four member sub-populations, and the positive value of predicting hospitalization increased to 20 percent.
The study shows that the researchers’ hospitalization model, used in combination with intervention plans for high-risk patients, may both improve patient outcomes and reduce insurance expenditures.
“These findings are exciting because they show the potential of big data in the health care setting,” says Dr. Welmoed van Deen, assistant professor of clinical medicine at the University of Southern California and co-author of the study. “We [showed] it is possible to use the data to create meaningful insights.”
Unlike many new technologies that are too expensive to implement, this model will actually save insurance money by predicting high-cost events before they occur, the researchers contend. Preventative treatment for at-risk patients is less expensive than preserving the status quo, which inevitably incurs expensive hospitalizations.
“This is a good example of how insurance companies, clinicians, and researchers’ interests can come together to improve patient care and save money,” says Dr. Jamie Feusner, a professor in residence at the University of California Los Angeles’s Department of Psychiatry.
Dr. Don Vaughn, the lead author and a computational neuroscientist at UCLA, points out that the consensus about the benefits of prevention is not new: “The problem has been in getting useful data.”
Medical data is fragmented between different labs, hospitals, and doctors, Vaughn says. By contrast, insurance companies have a comprehensive view and keep great records, giving insurers “the potential to be heroes.”
The results illustrate what’s possible from a cross-disciplinary team of clinical doctors, mathematicians and neuroscientists, the researchers add.
“Predictive medicine like this has the potential to improve medical outcomes,” says Dr. David Eagleman, neuroscientist. “Additionally, it could reduce healthcare costs and help overworked physicians. It’s a no-brainer.”