Predictive Analytics – A Proven Approach for Readmission Reduction

Readmissions to hospitals have become a major concern for hospitals and post acute care providers as it may lead to increased penalties. Most of the hospitals are striving hard to find suitable measures for reducing the rates of readmissions.

One of the most preferred and effective ways to readmission reduction is predictive analytics. It helps the hospitals quickly pinpoint patients who are at greater risk of readmission.

How predictive modelling works?

Readmission prediction is possible with the help of predictive analytics. Being a distinctive statistical technique, predictive analytics uses data mining and modeling to identify patterns and trends. With the help of complex calculations, patients who are at an increased risk of readmission can be shortlisted. Both, inpatient and outpatient environments could be considered to ensure more accuracy in predictions and in turn yield better outcomes.

On the basis of calculated risk scores, post acute care providers are able to proactively take measures towards readmission reduction. With daily reports obtained from predictive analytics, allotting resources when and where they are needed the most to focus on high-risk patients becomes very easy.Such an approach also helps reduce readmissions, save resources, and improve patient satisfaction while reducing suffering.