Using Big Data to Save Lives in Pennsylvania

Pennsylvania health care systems like Penn Medicine are using big data to save lives.

Penn Medicine’s big data project called Penn Signals is developing predictive analytics to diagnose deadly diseases before they start, per a Jan. 11 FierceHealthIt article. Penn Signals uses a homegrown clinical data warehouse that holds records on three million patients going back 10 years.

It is building predictive models based on this historical data. Penn Medicine does plan to make these models available outside the organization.

Applying Big Data to Real-World Health Care Challenges

This big data project is beginning to yield results. Two examples include:

  • A sepsis early warning system—Clinical studies show that the mortality rate for sepsis increases by seven percent every hour that a patient goes undiagnosed. Penn Medicine says that their early warning system which analyzes six vital sign measurements and lab values has led to a four percent reduction in sepsis mortality rates
  • Detecting patients trending toward cardiac failure—An algorithm helps detect 20 percent more patients trending toward heart failure and has identified patients who are five times more likely to be readmitted after heart failure

Learn How to Use Your Data to Improve Outcomes with Help from PAMED

Physicians and practices can also utilize their data in order to improve patient outcomes. The Pennsylvania Medical Society’s online program “Volume to Value: Making Sure Physicians Have the Skill to Succeed” offers practical advice and strategies that can be used in the transition to value-based care.

PAMED’s innovative CME series of six online, on-demand modules, free to PAMED members is facilitated by PAMED member and nationally respected expert Ray Fabius, MD. The courses cover important topics, including practical health informatics, using the data toolbox in your practice, quality management, process improvement, lessons learned from the managed care era, and population health. You can earn up to 1 credit of CME for each online course.