You know that consumer data is an extremely valuable asset. Likewise, you’re probably familiar with the hype surrounding machine learning technologies. To unlock the full potential of consumer data and machine learning, experimentation is key. Don’t get us wrong, machine learning is still valuable by itself. Its ability to use historical data to inform decisions about the future can be hugely beneficial for today’s Health Systems and their revenue cycles. Similarly, you can also experiment without machine learning. All an experiment requires is a change to a process or workflow and measuring the effect of that change.

When you combine machine learning and experimentation, they become much more powerful together. Machine learning can help your organization determine where it should experiment and then, test the experiment on a large scale to generate outcomes. These outcomes allow machine learning to fine-tune revenue cycle activities accordingly. As a result, your Health System gets closer to achieving its strategic goals with each adjustment.

Key Machine Learning Models

Regression to Support Payment Progression

Consider using machine learning regression models, such as logistic regression. Regression models can predict future outcomes by analyzing the relationship between data points. If the model’s predicted outcome falls short of your target outcome, you should experiment. You may find a way to close the gap and reach your target outcome, i.e. more patient payments. Health Systems can deploy this model to several areas of the revenue cycle, from eBill open rates to self-service payments.

For example, a regression model can help predict how likely a patient is to pay their bill, and therefore how many patients will pay their bills. If that number is low, running experiments may reveal a new cost-effective way to drive payments from more patients. Health Systems can test how pushing flexible payment plans or financing offers with low monthly payments within the billing statement influences patient behavior. Or, consider testing how adding a “Pay Later” option impacts payment rates. With these targeted experiments, your Health System can find the best route for achieving its revenue cycle goals.

Sometimes, Labels are a Good Thing

In addition, machine learning classification proves worthwhile. With machine learning classification methods, such as random forest classifiers, Health Systems can segment and label categories of data. This could be categories of patients, services, or even locations. By segmenting these categories, you can focus your efforts on the segments with the highest yield and understand how to better engage with each group.

This also makes your experiments much stronger because your organization can experiment with each segment and identify which factors impact patient payments. What impacts payments for one segment may not impact payments for another.

For instance, say you segment patients according to their bill balance and want to see how payment plan offers impact payment rates. You may find that payment plan offers only impact patient payments for bill balances above a certain dollar threshold. Or, perhaps you want to experiment with offering a prompt pay discount. Testing this option across several segments will help you identify which patients take advantage of the discount and pay in full. Such experiments enable Health Systems to truly tailor their patient financial communications in a way that drives payments.

A Dynamic Duo

Machine learning and experimentation are a dynamic duo. Together, they support smarter decision making and a better patient financial experience, which powers your Health System’s growth. If you’re curious about more ways you can experiment within your revenue cycle, check out our latest white paper.

Machine learning and experimentation can significantly impact your organization’s revenue cycle strategies, so it’s important to have a skilled team or partner that helps you leverage these tools effectively. The right partner will ensure your Health System properly prepares data for the machine learning models and accurately interprets the results. With those results, you can create a top-notch financial experience that promotes more patient payments and greater patient loyalty.