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New Propensity to Pay Technologies Can Reduce Payment Defaults from Self-pay

With revenue coming from self-pay increasingly becoming a large portion of the total revenue for healthcare providers, there’s an even greater need for innovative ways to approach the patient collection process. 

According to the National Association of Healthcare Access Management (NAHAM), self-pay is the third largest payer just behind Medicare and Medicaid. Patients now represent about 30 percent of healthcare revenue. 

Self-pay comes at a high price, especially for facilities looking to clamp down on cost and increase revenue. The cost of collection for self-pay is estimated to be three times that of commercial insurance. Moreover, a significant portion of self-pay balances go uncollected by providers and are eventually treated as bad debt.

Since this problem is connected to the growing financial responsibility of patients (many are unable to offset their medical bills without getting credit), it cannot be avoided. Surgery centers will still have to extend credit facilities to patients unable to settle their medical bills at the point of service.  

However, leveraging new technology has helped minimize the risk of default from patients while also simplifying the collection process for both patients and providers.

Recently, behavioral based propensity-to-pay models have been developed to overcome the limitation of accurately predicting the medical indebtedness of patients. The previous practice was to rely on credit scores to predict the probability of default by patients. Credit scores are however not well suited for this task as they focus more broadly on consumer debt.

This new technology relies on data from multiple sources to accurately predict patient’s likelihood of default. Based on their credit ranking, patients are then offered payment plans tailored to suit their ability to pay. 

Providers therefore have greater assurance of a lesser risk of default on the credit advanced to patients. This in turn results in win-win situation for both parties as their interests are aligned.

By being able to ascertain a patient’s ability to pay before surgery is conducted, an ASC can prevent default by engaging patients on the available payment options. Moreover, the time and effort spent unproductively on tracking collections from patients who are unlikely to pay will be significantly eliminated.

A facility can therefore re-prioritize by channeling more resources to patients with a higher probability of repaying their debts. This should in turn increase the facility’s revenue level while also improving their patient satisfaction ratings.

The increase in patient responsibility for medical expenses is changing the way self-pay is being approached by ambulatory surgery centers. At the core of this shift are innovative technology solutions that improve the collection process through computer-based algorithms.

Needless to say, centers will still have to engage with patients on a personal level to get information that software codes just cannot reveal.

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