Among the benefits of using predictive modeling:

• Improved productivity of nurses and case managers
• A reliable method of working the list of patients, instead of basing rounds on room number, time of discharge or other information
• Case managers can now be deployed based upon the complexity of the patients and their likelihood of readmission
• The ability to better manage work loads across floors and units
• Identifying the interventions also provides the back up information needed for transition of-care documentation

Why predictive analytics?

Predictive analytics has been gaining ground over the past several years. Simply put, predictive analytics uses a variety of statistical techniques including: modeling, machine learning, and data mining, that analyzes current and historical facts in order to make predictions about the future.

In the healthcare setting predictive analytics is effective in addressing such things as: length of stay management, preventable readmissions, and hospital acquired infections.

According to the Gartner Group, organizations that use predictive business metrics will increase their profitability by 20 percent by 2017. Nevertheless, predictive Analytics can also be challenging. The Aberdeen Group reports that predictive analytics can be difficult to operationalize because it can be complex and time consuming.

In addition, there is a shortage of data scientists. "Business can't see past these challenges to how predictive analytics will improve daily outcomes," the report stated. The Aberdeen Group also cites barriers to adoption of predictive analytics such as: lack of understanding of the benefits, lack of appropriate skills, and difficulty in quantifying ROI.

However, some industry observers believe that predictive analytics will continue to play a major role in the healthcare sector.

Peter Horner, the editor of Analytics Magazine, said in a 2012 article: "Skyrocketing costs have rendered the current U.S. healthcare system 'unsustainable,' market forces are calling for a performance-based system, analytics are crucial to this paradigm shift from 'volume' to 'value', and the transformation is inevitable."