We spend a lot of time posting about patient billing – how to increase payments, how to save money while you increase payments, trends in collecting patient payments, etc. – and for good reason. Collecting revenue that is owed by patients is a growing challenge for many healthcare providers. High deductible plans and escalating out-of-pocket amounts are contributing to the problem as patients bear a greater share of their medical costs. Unfortunately, this is a trend that is not likely to reverse itself any time soon.
A common mistake among providers is they continue to operate as if the process of getting paid begins with dropping a patient bill in the mail. The new reality is that if a provider wants to influence patient payment outcomes, the process of getting paid must start even before medical service is provided. I’m not talking about up-front copayments or pre-payments. I’m talking about strategically enhancing the ability to get paid – the entire patient portion – at minimal cost to the provider.
Providers can no longer simply send a bill and expect payment. They must know, in advance, what is going to happen with the revenue that is due post-service. Will it get paid in full? Paid over a period of time? Or not paid at all – regardless of the time and expense of the collection effort. Equipped with this knowledge, providers will know how to best proceed to collect the maximum amount of monies due. It is not just a single approach for everyone – it requires a multi-pronged strategy based on patient knowledge.
The trend is to run a quick credit report on patients to understand ability to pay. However, knowing patients’ history of timely payments on their mortgage and credit card bills does not tell you whether or not they have the ability or willingness to pay their medical bill. For that insight, you must turn to your own patient history data. Your experience with collecting amounts due from each patient is a superior indicator of what the future holds for your patient revenue.
Using predictive analytics, patients can be scored, in advance, based on their propensity to pay. For patients who have no history with a given provider, a score can still be determined using look-alike methodology. By applying this advanced knowledge, providers can create segment-specific strategies that will maximize the funds acquired, do it in the shortest period of time, and minimize the expense of acquiring those funds.
Likely to Pay Bill in Full: Send the patient a regular invoice. Withhold them from early-out to avoid paying a commission.
Likely Needs a Payment Plan: The patient receives payment plan options, and you can enable auto-pay for monthly installments.
Likely Qualifies for Charity: Assume write-off if no funds are received within a specified timeframe. You do not incur costs from external collection service providers.
There is nothing particularly groundbreaking about the process above. After all, traditional predictive models have been used for ions – each with their own approach, method and underlying data. What is new is the data science and breadth of underlying data driving the efficacy and reliability of the prediction.
Hundreds of predictive variables are in play when it comes to patient behavior. Many models in the marketplace today are built using just a handful of data points – primarily centered on credit history and demographics of the patient. While those variables may contribute to the prediction, they don’t deliver the full picture of a patient’s propensity to pay their medical bill.
So, how is a provider to know which model to use? The approach Wind River Healthcare takes with our predictive models has been successful:
There are millions in revenue at stake, and healthcare providers simply can’t afford to jeopardize it by taking action based on a partial picture. If you have questions about implementing strategies based on patient propensity to pay, give a call or shoot me an email. I’d be happy to help in any way I can.