JT on EDM

I saw a post today on medical credit scoring that made me think I should post something about how credit scoring can be used in healthcare. Now saying that, of course, makes everyone nervous – are we talking about refusing people treatment because of their credit score? Why should financial questions like credit worthiness have a place in the delivery of healthcare.

Well as much as 30% of patient payments end up being written off as bad debt so whether you are a for-profit or a not-for-profit hospital this is a problem. Not only do you have to spend money on debt collection (money you could have spend on health care) you also have to borrow money to cover the short fall. Using a enterprise decision management (EDM) approach and taking advantage of credit scores is one way to address this.

During the admissions process, decision management applications can perform real-time validation of patient-supplied data against medical records and external data sources. By making certain patients are who they say they are and checking that address and other information is complete and correct, providers reduce their fraud risk. Using credit scores and other predictive analytics, an EDM solution can determine the optimal initial payment request for patients based on their particular financial situation. Treatment delivery is separate – this is just about how the patient is going to pay (bill them later, take their credit card, ask for cash or help them with a charitable application). Knowing which patients can and will eventually pay what helps financial counselors to work with patients who need help and minimize the number who go to collections, while also generating maximum revenue for the hospital.

Once a patient is discharged, an EDM approach can also be used with overdue accounts to improve collection results and minimize recovery costs. Instead of treating all overdue accounts with the same sequence of dunning letters and calls, providers may, in fact, be able to collect more money by doing less. Analytics can identify differences between accounts that affect payment behavior-dividing patients up into those likely to self-correct, those likely to be influenced by collections treatments and those unlikely to pay under any circumstances. Providers can use this segmentation to save money by making fewer outbound contacts and thereby also reducing the volume of inbound inquiries such contacts generate.

This is similar to the usual use of credit decisioning except that you don’t want to decline care because someone can’t pay so much as help them pay for the care they need.