The rise mirrors
recently reported trends seen in papers using publicly available health data sets, which are authored primarily by researchers in China. But TriNetX is only open to users at participating health care organizations, and most TriNetX papers come from authors at U.S. medical schools, often with a physician-in-training as lead author. Medical schools use TriNetX as a research training ground, and the resulting papers are a relatively easy way for medical students to boost their CVs before applying for residencies. “There is no substitute for learning this process than by doing it,” says Lisa Howley of the Association of American Medical Colleges (AAMC).
But the combination of inexperienced users and TriNetX’s push-button analysis tools can lead to shoddy publications, which often do not correct for potential biases that can make treatments appear more effective than they are. And because the data can be analyzed so quickly, users can easily cherry-pick positive results for publication, a practice known as p-hacking. “The flow of false discoveries is hugely greater,” says Matt Spick, a health-data scientist at the University of Surrey.
“My biggest concern,” Wang says, “is that doctors in 10 years’ time want to look into a particular concept and they go into the literature and everything is just associated with everything.”
