At A Glance
Propensity-to-pay models use predictive analytics to help healthcare organizations understand patient payment behavior. Learn how providers can leverage these tools to prioritize collections, improve cash flow and reduce bad debt.
Key takeaways:
- Providers are facing increasing bad debt levels and a sharp decline in patient collection rates.
- Propensity-to-pay models use predictive analytics to quickly help collections staff prioritize patient accounts with the highest likelihood-to-pay.
- In 2024, Experian Health’s Collections Optimization Manager clients achieved an exceptional Return on Investment (ROI) of 10:1.
Introduction
Inefficient collection processes and a lack of insight into 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 analytics in healthcare collections can help revenue cycle leaders better forecast the likelihood a patient will pay, streamline the entire collections process and boost revenue recovery rates. Understanding a patient’s individual and unique financial status through the use of propensity-to-pay models will lead to a more strategic outreach, resulting in higher patient satisfaction.
Here’s everything healthcare organizations need to know about using propensity-to-pay models powered by machine learning, like Experian Health’s Collections Optimization Manager.
Why propensity-to-pay models matter more than ever
As healthcare providers face continued staff shortages, juggle high volumes of self-pay accounts and adjust to new regulations under the One Big Beautiful Bill Act (OBBBA), streamlining collections is critical. Without a modern propensity-to-pay model in place, collections timelines can drag out, disrupting the entire revenue cycle and affecting the quality of patient care.
Leveraging propensity-to-pay models, like the ones that are included within the Collections Optimization Manager, allows busy billing teams to easily identify which patients are most likely to pay and focus on collections for high-priority accounts. It also significantly reduces the dependency on third-party agencies, allowing you to keep more collections in-house, while eliminating wasted effort on low-yield tasks, such as repeated phone calls to accounts unlikely to pay.
Discover how Weill Cornell Medicine and Experian Health implemented a smarter collections strategy that delivered $15M in recoveries — and how you can do the same. This on-demand webinar shows how to move faster, work smarter and collect more, without adding headcount.
The data science behind propensity-to-pay modeling
The data science behind propensity-to-pay modeling may include the following:
- Data collection: Propensity-to-pay models utilize high-quality and comprehensive data from numerous internal and external sources such as ERP systems, CRM platforms, credit bureaus and employment status.
- Feature engineering: Data scientists identify raw data points that correlate strongly with payment behavior as features to use propensity-to-pay modeling.
- Model selection: Different types of algorithms can analyze data and provide propensity-to-pay score modeling. These include simple models to determine if a patient “will pay/will not pay” and more complex machine learning capable of spotting patterns to better predict payment likelihood.
- Model training: Before use, the model must be trained on historical datasets to determine the relationship between a feature and outcome, then validated for accuracy.
- Scoring and integration: After validation, revenue cycle managers can use the model to generate propensity-to-pay scores that indicate how likely a patient is to pay, prioritize high propensity-to-pay accounts and plan patient communication strategies.
What do machine learning/predictive analytics look at?
In propensity-to-pay modeling, machine learning and predictive analytics look at a wide range of factors to determine how likely a patient is to pay. These factors may vary by solution, but typically include:
- Demographics: Patient age, geolocation, income and socioeconomic data are all considered.
- Previous payment behavior: Historical drivers of future payment data, like payment history, payment success rate, payment methods and delays are factored into the modeling.
- Communication history: The model also considers past interactions like patient responses to collection notices, self-pay portal visits and the number of clicks on collection emails.
- Financial distress signals: Some models can also factor in behaviors that show changes in spending patterns and other indicators that a patient may struggle to pay.
The role of machine learning and AI in healthcare collections
Artificial intelligence (AI) and machine learning (ML), a subset of AI, both play a pivotal role in healthcare collections. When used in propensity-to-pay models, AI and machine-learning algorithms process vast amounts of data points and generate more accurate propensity-to-pay scores than less sophisticated scoring models.
Understanding ML vs. AI
The term “machine learning” is used interchangeably with AI. However, in healthcare predictive analytics, ML is a subset of AI where systems learn patterns from data without the need for explicit programming. Machine learning is commonly used in propensity-to-pay scoring solutions, like Experian Health’s Collections Optimization Manager.
It examines various types of information, then “learns” which patients are more likely to pay their bills and identifies those who may struggle to do so. The result is a propensity-to-pay score, a number that tells providers how likely each patient is to pay.
Looking to enhance self-pay collections and streamline your revenue cycle? Discover how Novant Health and Cone Health achieved 7:1 ROI and $14 million in patient collections with Collections Optimization Manager.
Experian Health’s unique data advantage
Various data models are used across the industry to predict a patient’s propensity-to-pay. However, Collections 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’s Collections Optimization Manager also brings together many types of data via its algorithms and analytic models. This helps providers better understand their patients’ financial factors, one patient at a time. When segmentation is properly deployed and utilized, the collections process becomes a better-informed interaction between a patient and their provider.
In a recent interview on patient collections technology, Experian Health’s lead product manager Matt Hanas notes:
“When providers use detailed, comprehensive segmentation, they can implement specific contact strategies, payment plans or even automatic write-offs based on a patient’s unique financial status. They can ensure that each patient has the right number of touches and can offer them a range of possible payment options.”
Matt Hanas, Lead Product Manager at Experian Health
FAQs
A propensity-to-pay score is a metric used in healthcare revenue cycle management to predict how likely each patient is to pay, so providers can prioritize collections efforts. Propensity-to-pay scores use machine learning and predictive analytics to spot trends based on factors such as payment history, credit, behavioral, socioeconomic and income data.
Machine learning and predictive analytics go hand-in-hand in healthcare revenue cycle management to help providers streamline collections. Machine learning models, like Experian Health’s Collections Optimization Manager, analyze a patient’s past payments, credit history, income data and other factors to spot patterns and use predictive analytics to gauge how likely the patient is to pay their bills.
Experian Health’s Collections Optimization Manager uses machine learning, a subset of AI, to generate propensity-to-pay scores for patients. These scores give providers a comprehensive view of a patient’s financial situation and help healthcare providers segment patients into tiers based on how likely they are to pay.
The bottom line: Embracing change in collections practices with Experian Health
Making updates to longstanding collections practices is a significant investment for most providers—and may feel like an intimidating undertaking. However, partnering with Experian Health to integrate a comprehensive collections solution powered by machine learning can help improve collections rates rapidly and lessen the administrative load. Our industry-leading tool, Collections Optimization Manager, offers a smarter and faster way to collect patient payments, and experienced consultants are available to support shifting collections needs.
Learn more about how Experian Health’s data-driven patient collections optimization solution uses machine learning and AI to help revenue cycle management staff collect more patient balances.
