Using Predictive Analytics in health care holds huge promise for improving care quality and outcomes for patients.
This type of data science can help care teams and hospital systems manage population care for chronic conditions, proactively identify patients at risk for disease, infection or hospital readmission, and observe trends in quality and outcomes. There is no shortage of information; the tough part is making the information actionable and knowing in advance what you are going to do with it, such as having a care management team in place to receive and take action on the data.
Kaiser Permanente has been using predictive analytics for years, leveraging our robust electronic health record, integrated systems and coordinated care teams. Two articles, Taking Predictive Analytics to the Next Level and How Predictive Analytics Can Help Prevent Infection describe the challenges and opportunities for working with this type of data, and ways it is currently used at Kaiser Permanente and other institutions to identify and target patients at risk for preventable events such as hospital readmissions and central line infections.
(Photo:Michael Kanter, M.D)
The tough part of predictive analytics is making the information actionable, says Michael Kanter, M.D., executive vice president of quality and chief quality officer of the Oakland, Calif.-based Permanente Federation.
To learn more about predictive analytics, check out the following links: