An inherent tension lies between using medical records for legitimate clinical research and concerns about patient privacy. “The problem is, stuff that’s considered anonymous really isn’t,” says Michael Swiernik, director of medical informatics at the University of California, Los Angeles. “It’s going to take a lot of different creative approaches to protect people, and this algorithm is one tool in that box.”
The new approach has its limitations. The studies work best, say the researchers, when they start out with a specific hypothesis or goal–say, to study the prevalence of asthma in teenagers with allergies. However, if they wanted to use the same data to examine associations between two random health issues in the future, it would be more difficult.
The researchers want to combine their clinical-code-protecting algorithm with other security mechanisms already in place, like protections for demographic information, to keep patient data as safe as possible. They also want to reach out to use more data outside of Vanderbilt, according to Grigorios Loukides, the study’s lead author.
The future of science relies on more subtle ways of extracting useful information from existing data. Methods that allow researchers to be more nuanced in how they anonymize data “enable us to maximize the scientific benefit we get from population data while controlling the risks to privacy,” according to Isaac Kohane, director of the Boston Children’s Hospital Informatics Program. It’s all about sharing, says study author Malin. “Generating data is expensive, and it’s both good science and good etiquette to reuse data. The challenge is to do it while protecting people.”
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