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Identifying a patient’s propensity to pay (or likelihood to pay) offers the opportunity to have an open and upfront conversation with your patients to discuss payment options.
Knowing a patient’s payment history can help providers establish a customized payment plan that fits the individual and sets them up for success rather than a painful process that will leave neither party satisfied.
It’s not even a question. Healthcare providers need to arm themselves with better data and offer better payment solutions to their patients.
The importance of propensity to pay and how to effectively grade each patient is becoming even more vital as both healthcare costs and the usage of higher deductible health plans (HDHP) increase.
These increases are putting an even greater strain on patient finances, which means healthcare providers need to arm themselves with better data and offer better payment solutions to their patients.
Just look at some of the recent trends, and you’ll understanding why a patient’s propensity to pay is critical to healthcare providers.
According to a TransUnion analysis, patients have experienced an 11 percent increase in out-of-pocket costs in 2017. The analysis further indicates that 39 percent of healthcare visits cost between $501 and $1,000. Twelve percent were more than $1,000. That means that over 50 percent of visits will cost more than $500.
Additionally, a Federal Reserve Economic Report shows that over 10 percent of adults are carrying debt from medical expenses, while another 44 percent of adults could not cover an emergency expense over $400. Despite an improving economy, Americans aren’t saving enough money.
Wind River has found that behavior-based, propensity-to-pay models offer a much broader, more encompassing view that improves predictability and enables several key strategies to reduce costs and efforts in the collection process. Health organizations can leverage their data and put it to work to improve their own payment outcomes while significantly reducing write-offs and collection costs.
Credit-based models have long been used, but credit scores only address consumer debt and purchasing behavior. They don’t accurately predict medical payment.
Behavior-based models allow health systems to predict a person’s ability to pay with high level of accuracy by gathering information from multiple sources while keeping patient information safe and secure.