The challenge for the Carolinas Healthcare System was to reduce the readmission rate for patients with chronic obstructive pulmonary disease.
The solution: predictive analytics.
One of the biggest problems, according to Jean Wright, chief innovation officer at CHS, was in identifying patients at risk of readmitting before they leave the hospital and enable care providers to intervene. Faced with CMS readmission penalties the healthcare system had to take swift action. “We knew at that point in time that COPD was not one of the conditions that was currently being measured and penalized under the CMS system but we knew it was on the agenda for 2013 and 2014,” said Wright.
CHS selected San Juan Capistrano, Calif.-based Predixion Software. Predixion utilizes Machine Learning Semantic Model that enables predictive applications to be quickly implemented into production environments, making the predictive process, according to the company, easily adaptable and repeatable.
Nish Hartman, director of healthcare at Predixion Software said that preventable hospital readmission costs $25 billion in wasteful spending. “What makes prediction impactful, said Hartman, “is what we call the last mile. This is serving up the risk scores in interventions in a very clear and simple-to-use interface. This is where the true value of your data is realized at the point of care with actionable insights delivered in real time that can be acted upon immediately.” An action plan was implemented and CHS rolled out a model in 2013.
Wright told participants at a webinar titled: Bringing Predictive Analytics to the Point of Care, on Sept. 10, (Watch Now) that initial implementation was completed in six months and included seven hospitals. The solution is now fully deployed at 13 of CHS’s metro-based hospitals where 100,000 plus patients have been managed with clinicians knowing their 30-day risk for readmission.
Wright said that in a year and a half to a two-year period CHS was able to drop the readmission rate from 21 percent to 14 percent.
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