How do predictive analytics improve propensity-to-pay scores in healthcare RCM?

Key takeaways:

• Providers are facing rising levels of bad debt and a sharp decline in patient collection rates.
• Propensity to pay models use predictive analytics to quickly help collections staff prioritize patient accounts most likely to pay.
• In 2024, Experian Health Collection Optimization Manager Clients achieved an exceptional return on investment (ROI) of 10:1.

Inefficient collections processes and a lack of knowledge about a patient’s propensity to pay can disrupt the entire healthcare revenue cycle, leading to cash flow issues, bad debt, and poor financial experiences for patients. However, predictive analysis in healthcare collections can help revenue cycle leaders better forecast the likelihood of a patient paying, streamline the entire collections process, and increase revenue recovery rates. Understanding a patient’s individual and unique financial status through the use of propensity to pay models will lead to more strategic outreach, resulting in greater patient satisfaction.

Here’s everything healthcare organizations need to know about using machine learning-powered propensity to pay models, like the one from Experian Health. Collection Optimization Manager.

Why propensity to pay models matter more than ever

As healthcare providers face continued staffing shortages, they must juggle large volumes of self-pay accounts and adapt to new regulations under the Big Beautiful Bill Act (OBBBA), Rationalizing collections is essential. without a modern propensity to pay model Instead, collection times can be prolonged, disrupting the entire revenue cycle and impacting the quality of patient care.

Take advantage of propensity to pay models, such as those included within the Collection Optimization Managerallows busy billing teams to easily identify which patients are most likely to pay and focus on collections on high-priority accounts. It also significantly reduces reliance on third-party agencies, allowing you to keep more collections in-house while eliminating wasted effort on low-performing tasks, such as repeated phone calls to accounts that are unlikely to pay.

Learn how Weill Cornell Medicine and Experian Health implemented a smarter collections strategy that generated $15 million in recoveries, and how you can do the same. This on-demand webinar shows how to move faster, work smarter, and raise more—without adding staff.

The data science behind the propensity to pay model

The data science behind the propensity to pay model may include the following:

• Data collection: Propensity to pay models use comprehensive, high-quality data from numerous internal and external sources, such as ERP systems, CRM platforms, credit agencies, and employment status.
• Feature Engineering: Data scientists identify raw data points that strongly correlate with payment behavior as features for using propensity to pay models.
• Model selection: Different types of algorithms can analyze data and provide propensity to pay scoring models. These include simple models to determine whether a patient will “pay or not pay” and more complex machine learning capable of detecting patterns to better predict the likelihood of payment.
• Model training: Before use, the model must be trained on historical data sets to determine the relationship between a feature and an outcome, and then validated for accuracy.
• Scoring and integration: After validation, revenue cycle managers can use the model to generate propensity to pay scores that indicate the likelihood that a patient will pay, prioritize accounts with high propensity to pay, and plan patient communication strategies.

What do machine learning and predictive analytics analyze?

In propensity to pay models, machine learning and predictive analytics analyze a wide range of factors to determine the likelihood that a patient will pay. These factors can vary depending on the solution, but typically include:

• Demography: The patient’s age, geolocation, income and socioeconomic data are considered.
• Previous payment behavior: Modeling takes into account historical factors of future payment data, such as payment history, payment success rate, payment methods, and delays.
• Communication history: The model also considers past interactions, such as patient responses to collection notices, visits to the self-payment portal, and the number of clicks on collection emails.
• Signs of financial difficulties: Some models may also take into account behaviors that show changes in spending patterns and other indicators that a patient may be having difficulty paying.

The role of machine learning and AI in healthcare collections

Artificial intelligence (AI) and machine learning (ML), a subset of AI, play a critical role in healthcare collections. When used in propensity to pay models, AI and machine learning algorithms process large amounts of data points and generate more accurate propensity to pay scores than less sophisticated scoring models.

Understanding Machine Learning vs. Artificial Intelligence

The term “machine learning” is used interchangeably with AI. However, in healthcare predictive analytics, machine learning is a subset of AI in which systems learn patterns from data without the need for explicit programming. Machine learning is commonly used in propensity to pay scoring solutions, such as Experian Health. Collection Optimization Manager.

It examines various types of information, then “learns” which patients are most likely to pay their bills and identifies those who may have difficulty doing so. The result is a propensity to pay score, a number that tells providers how likely each patient is to pay.

Looking to improve self-payment collections and optimize your revenue cycle? Learn how Novant Health and Cone Health achieved a 7:1 ROI and $14 million in patient collections with Collections Optimization Manager.

Experian Health’s Unique Data Advantage

Across the industry, various data models are used to predict a patient’s propensity to pay. However, Collection Optimization Manager uses a more robust data set for modeling, providing a unique data advantage. This solution segments patients by credit data, payment history, demographics, and more, making it a more powerful tool for revenue cycle managers.

Experian Health Collection Optimization Manager It also brings together many types of data through its algorithms and analytical models. This helps providers better understand their patients’ financial factors, one patient at a time. When segmentation is implemented and used properly, the collections process becomes a more informed interaction between a patient and their provider.

in a recent interview on patient collection technologyMatt Hanas, senior product manager at Experian Health, notes:

“When providers use detailed, comprehensive segmentation, they can implement specific contact strategies, payment plans, or even automatic cancellations based on a patient’s unique financial status. They can ensure each patient has the right number of contacts and offer them a variety of potential payment options.”

Matt Hanas, Senior Product Manager at Experian Health

Frequently asked questions

The bottom line: adopting changes to collection practices with Experian Health

Updating long-standing collections practices is a significant investment for most providers and can seem like a daunting task. However, partnering with Experian Health to integrate a comprehensive collections solution powered by machine learning can help improve collection rates quickly and reduce administrative burden. Our industry leading tool, Collection Optimization Manageroffers a smarter, faster way to collect payments from patients, and experienced consultants are available to support changing collection needs.

Learn more about how Experian Health’s data-driven solutions patient collection optimization solution uses machine learning and artificial intelligence to help revenue cycle management staff collect more patient balances.