Last year the Journal of the American Medical Informatics Association ran an article about post-operative nausea, showing that by looking at the right data beforehand, providers can sometimes prevent or ease that misery for their surgical patients. While existing nausea-prediction models only use patient characteristics and medical history, which can’t be changed, the authors identified several points where the choice of anesthetic or pre- and post-op medications had a clear effect on the patient’s level of nausea. They suggested eventually building their model into decision support for anesthesia systems.

The data for the study was laboriously collected by hand—16 data points for each of 2,505 surgical patients at one academic medical center over two years. At that rate, improvements of any kind will come slowly. But with the advent of electronic health records, such studies could be carried on in real time, with treatments tweaked continuously based on a growing volume of data across many providers. In the operating room, experts say the payoff from predictive analytics could be spectacular, since surgery-related expenditures account for almost a third of health care spending.

Register or login for access to this item and much more

All Health Data Management content is archived after seven days.

Community members receive:
  • All recent and archived articles
  • Conference offers and updates
  • A full menu of enewsletter options
  • Web seminars, white papers, ebooks

Don't have an account? Register for Free Unlimited Access