Health informaticists at UC-San Francisco have tracked down the source of many cases of Clostridium difficile infection that had vexed a UCSF hospital. The clues—there for the piecing together in the EMR—led to a CT scanner in the emergency department.
The numbers behind the effort, reported in a research letter published online Oct. 23 in JAMA Internal Medicine, nicely illustrate the power of big health data when meaningfully mined.
The informaticists were able to trace the movements of more than 85,000 patients who had well over 400,000 location changes and had an overall C. diff infection rate of 1.3 percent (1,152 patients) over a three-year period.
The study authors found that patients imaged on the implicated CT scanner within 24 hours after C. diff-positive patients were more than twice as likely to become infected.
“This effect remained significant after adjustment for covariates and in sensitivity analyses extending the incubation period to 72 hours,” Sara Murray, MD, and colleagues write. “Trends in other areas did not reach statistical significance, and the effect was not significant in an adjusted hospitalwide analysis.”
The team further uncovered what had gone wrong with the C. diff-spreading CT scanner: It hadn’t been updated to match the standardized methods applied in other radiology suites.
“This shows the potential for what can happen when thoughtful data scientists leverage electronic health records to tackle a common health care problem,” Niraj Sehgal, MD, MPH, vice president and chief quality officer for UCSF Health, says in an article posted on a UCSF News Center page.
The C. diff bacterium is one of the most common causes of hospital-acquired infection. In many cases its only symptom is bothersome diarrhea, but severe cases aren’t rare and can result in death.