Patient Payments

Stop Using Credit Scores For Propensity To Pay

Post by
Justin Nicols

Credit scores have little bearing on patients ability to pay a medical bill. Soft credit, hard credit, it doesn’t matter.

Sift data shows that only ~17% of patients pay their medical bills in the early-out period. Medical expenses are simply not part of the average person’s monthly budget. Patients are unlikely to pay medical bills that are greater than 5% of their household income. And, 78% of Americans report that they live paycheck to paycheck.

The circumstances that lead to unpaid medical bills are different from the drivers of other forms of consumer debt. The Consumer Financial Protection Bureau reports that most consumers with medical debts show no other signs of financial distress. 50% of consumers who have medical trade lines, had previously “clean” credit reports with no past delinquencies.

Consumer debt on credit reports is not an indicator of a consumer’s ability or willingness to pay their medical bills. However, credit scoring models may treat medical trade lines the same as non-medical collections. A credit score may tell you whether someone can pay their mortgage, but that doesn't mean they can also cover unexpected medical expenses.

Most propensity to pay models primarily rely on credit scores. This scoring is faulty and doesn’t customize its approach to healthcare or to a provider’s unique patient population mix. Propensity to pay scores should use a variety of attributes. Most of these attributes should come from a provider’s historical data.

Providers and RCMs need to know how *their* payments perform — taking into account the uniqueness of their facilities, providers, specialties and regions.

When you move away from credit scores and look at patient data,  propensity to pay accuracy increases. You also unlock the ability to determine not only who will pay, but how much each individual will likely pay, who needs a payment plan and how to best contact individual patients.

Using a combination of historical payments data and external data, Sift's Propensity To Pay models predict patients' propensity to pay with increased accurancy and compliance with regulations. We don't rely on credit scores -- neither should you.

Learn more about Sift Healthcare's approach to Propensity To Pay and Patient Payments Management.

More From Blog

You Might Also Like

Claim Denials
The 25% Challenge
Read More
Healthcare Payments
6 Ways To Use Your Healthcare Payments Data To Improve Collections
Read More
Revenue Cycle Analytics
Filling The Gap In Revenue Cycle Reporting
Read More

Stay in the know

Join our newsletter for industry and company news, product announcements, and more.

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.