Are you getting the most out of your predictive score(s)?

Chances are you’re probably not – but that is understandable.

In comparison to other industries, healthcare revenue cycle is still relatively “young” when it comes to deploying big data and predictive models to improve business operations. Luckily, predictive analytics has been a staple in other industries for years. And many organizations in these industries are mastering the execution of predictive models.

Here are three tried and true applications of predictive modeling used in other industries, and how they can be used in healthcare revenue cycle.


1.  Optimize patient payment arrangements to accelerate cashflow and account resolution.

In the credit card industry, predictive analytics are used to determine the APR of their cardholders.

Credit card companies have multiple predictive models in place that account for different factors about their cardholders, such as:

  • their overall debt load
  • their past history with the bank or lending institution
  • the type of purchases they have made with credit cards

All of this data is then fed into a larger segmentation strategy to determine the cardholder’s APR.

The takeaway for RCM professionals

While hospitals can’t leverage such a sliding scale because of 501(r) regulations, they can use similar models and scores to determine whether or not to offer prompt-pay discounts or automate payment plans.


2.  Automate follow-up to at-risk populations to increase likelihood of response.

In the retail industry, how many times have you put an item in your online shopping cart only to close the browser and abandon the purchase altogether?

Some of the most innovative retail companies are using statistical models to get online shoppers to return to their site and complete the purchase.These statistical models predict whether or not a person will return to the site and make a purchase if he or she receives an automated reminder email in real-time.

Sometimes the email is a reminder that an item was left in their shopping cart, and other times it offers a discount for completing the purchase. The model dictates the offer that is presented in the email.

The takeaway for RCM professionals

For hospitals using predictive models, sometimes patients fall into the “gray area” – do they qualify for presumptive charity? What about their likelihood to pay?

You may not have enough information to make a clear determination. However, if you know that a patient went to your online portal to pay or see their phone number in your IVR reporting, you can automate a follow-up phone call, text, or email to encourage them to finish their transaction if they did not make payment.


3.  Create a sense of urgency by alternating communications based on the score.

The travel industry, particularly airlines, use a variety of predictive models based on third-party data to customize all offers and customer communications.

They account for factors such as:

  • credit bureau data
  • online activity
  • past purchase behavior
  • time of year

The model is used to both tailor the offer to prospective flyers and create urgency – leading to more booked flights for the airline.

The takeaway for RCM professionals

Hospitals can adopt a similar strategy based on propensity to pay scoring. If you know a patient is likely to pay, as well as a seasonal time when they may have more disposable income (i.e. tax refund season), try creating a sense of urgency in your communications.

Example: Save 20% on your bill by making a payment before April 20, 2015.


Given the current RCM landscape, leveraging analytics can seem so overwhelming that it’s too much to tackle right now with your current workload.

However, the right partner can automate all of the workflows in the scenarios described above. Using data to automate and improve the patient’s RCM experience may seem like science fiction right now, but it isn’t. Ask your solution provider how they can help or feel free to reach out to RevSpring for more information.


About the Author: April Wilson is the Director of Analytic Products at RevSpring. She uses her diverse data analytics background to help hospitals measure, optimize, and automate their patient communication effectiveness and RCM business processes.

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