Leveraging Predictive Analytics to Fight Bad Debt

RevSpring

On June 16, 2013 at the Healthcare Financial Management Association’s annual ANI conference, keynote speaker Donald Berwick told the audience that the levels of waste in the existing healthcare system were shameful. His research estimates that about a third of all spending goes to waste due to process inefficiencies and fraud. While pricing is a big area for improvement, there are tools that exist right now to help hospital patient finance directors reduce bad debt as a result of poor processes or fraud.

Predictive analytics is not a new concept

Financial services have leveraged predictive analytics since the mid-80’s. Most of us are familiar with this concept in the form of a credit score. However, there are other tools for healthcare professionals outside of the credit score, specifically designed to help identify charity care patients or identity theft.

Charity scoring

Charity scores use your hospital’s Financial Assistance Policy as the foundation for all predictive modeling. They work by pulling publicly-available third-party data such as geographic, tax, and other demographic data into a centralized database and then scoring patients based on their likelihood to qualify for charity according to your hospital’s policy. Implementing charity score predictive modeling at point-of-service or in the back-end billing cycle can be done without making any process changes. It is a quick and easy way to identify accounts that would otherwise roll to bad debt due to incomplete or missing charity application data being returned by the patient.

Identify verification

Like charity scoring, identify verification can also be run anywhere in the existing process. This model runs patient information against the social security database and other fraud-detection services to predict the likelihood that the information given at point-of-service is fraudulent or bad. This can lead to significant cost savings on the back-end by reducing returned mail and printing costs on patients where it is highly probable that the hospital has the wrong contact information.

Taking the next step

Better detection of charity care or fraud can also help with 501(r) compliance. Talk to your patient communications team to understand if you are currently using predictive analytics to help reduce waste. If you are not currently leveraging healthcare analytic models, now is the perfect time to start.

About the Author

April Wilson has a long history of measuring and optimizing customer communication for top brands, and she has built her career around evangelizing the power of data and using consumer insights to change behavior.