How propensity-to-pay models help health care providers improve collections
How propensity-to-pay models help health care providers improve collections
By natalie lima | Published: 2025-09-30 10:00:00 | Source: Healthcare Blog
Main takeaways:
- Healthcare organizations face increasing levels of bad debt and sharp declines in collections.
- Propensity-to-pay models using machine learning and robust data provide insight into a patient’s likelihood to pay and allow staff to focus their collection efforts where they matter most.
- In 2024, Experian Health customers implemented Collections Optimization Manager Saw a 10:1 ROI. Some clients, such as Weill Cornell Medical College, have seen recoveries of up to $15 million.
Healthcare organizations are facing a sharp decline in collections and an increase in bad debt. Contributing factors include rising self-pay costs and an increasing number of patients struggling to afford their medical bills. inefficient Collection practicesreliance on third-party agencies that do not use payment propensity scores and manual processes is also a major contributor to this growing market problem. Providers who adopt Propensity-to-pay models Companies that use data and automation to predict payment likelihood often see improved revenue recovery and patient satisfaction.
Here’s what to know about propensity-to-pay collection strategies in health care.
Why propensity to pay matters in health care groups
Propensity to Pay is a data-driven model that identifies patient groups with a greater likelihood of paying, to enhance existing collection strategies. When billing teams better understand a patient’s propensity to pay, they can more easily prioritize communication and allocate collection resources effectively. This reduces their workload, as they can focus their efforts where they will have the greatest impact, and on accounts with the highest potential to pay. Keeping more collections in-house also reduces reliance on expensive outside agencies, while eliminating wasted effort on low-return tasks – such as repeated phone calls or statements mailed to accounts unlikely to pay. The need to adopt propensity-to-pay models has increased in recent years as patient numbers and costs of care continue to grow.
In the past 20 years, US hospitals have absorbed approx $745 billion in uncompensated care, According to data from the American Hospital Association.
Rising health care costs and the new lawBig, beautiful billIt is expected to shift more financial responsibility to both hospitals and patients.
Unfortunately, many organizations still rely on ineffective collections processes and third-party agencies Medical billing practices Which lacks propensity-to-pay insights. The result? Total disturbances Revenue cycleincluding lost patient revenue, wasted resource hours, increased collection costs, and higher vendor costs. The use of outdated collection strategies also contributes to patient dissatisfaction and churn, causing further revenue leakage.
Why healthcare providers need propensity-to-pay analyses
Limited staff capacity and large quantities of Self-pay accounts This increases the complexity of collections faced by institutions that have not yet adopted propensity-to-pay analyses. As collection timelines drag on, providers can face cash flow issues, revenue losses, and bad debts.
This ultimately disrupts the revenue cycle and impacts the quality of patient care – and the entire patient experience. By leveraging propensity-to-pay analytics, revenue cycle leaders can enhance… Revenue cycle predictability and Streamline collection efforts.
Listen as Weill Cornell Medicine and Experian Health discuss how a smarter collections strategy led to a $15 million recovery—and how you can do the same. This on-demand webinar shows how to move faster, work smarter and collect more, without adding headcount.
How propensity-to-pay models work in practice
Propensity-to-pay models examine patients’ accounts and segment them based on their probability of paying. Segmented accounts receive a propensity to pay score – from 1 to 5, with 1 representing the highest probability of payment – and are then transferred to the appropriate settlement channels.
Experian Health Solution, Collections Optimization Managerleverages machine learning, predictive analytics, and data sources — such as credit, behavior, and demographics — to identify patient accounts that have the highest likelihood of payment. It also automatically scans patient data for deceased, bankrupt, Medicaid, and.charity.
Patient accounts are then sorted into payment groups through data-driven segmentation. This allows busy collections staff to quickly clean up accounts receivable and put their focus on what matters most – the patient accounts that have the strongest chance of paying their bill.
With a clear picture of the patient’s financial situation, healthcare organizations can do this Improve communication with the patient And continue to enhance collection efforts to maximize revenue. High propensity accounts may receive gentle reminders, such as less frequent bill reminders. Meanwhile, alternative financial assistance, such as charitable sponsorship or… Payment planscan be automatically made available to low propensity patients.
Benefits of using propensity to pay models
Propensity-to-pay models, such as the Experian Health model Collections Optimization Manager The solution offers numerous benefits to organizations that enhance the revenue cycle.
- High collection rates: Using a propensity-to-pay model makes AR more manageable, especially for organizations with a large patient volume. Free tools, such as Experian Health Patient request and patient text, Easily send self-pay options via voice or text messages, enhancing patient engagement and building trust.
- Reducing bad debts: Propensity-to-pay models help identify patients who have a low probability of paying their medical bills.
- Lower collection costs: Chasing payments on deceased, bankrupt, or Medicaid-eligible or charitable accounts wastes valuable resources. Using propensity-to-pay models, busy employees can efficiently work on high-yielding accounts within the company, reducing the number of accounts that need to go to third-party vendors.
- Faster cash flow: Prioritize patients who are likely to pay early and shorten payment cycles, which can improve revenue cycle predictability.
Implementing propensity to pay analyses: best practices
Healthcare organizations implementing propensity-to-pay analyzes should consider the following best practices:
- Choose the right partner. Look for a technology partner, like Experian Health, with extensive data assets and healthcare experience.
- Automating patient communication. Reduce overhead expenses and increase collection efforts with automated patient communication strategies.
- Ensure compatibility with legacy technology. For real-time accuracy, choose a solution that integrates seamlessly with existing electronic health records and billing systems.
- Training of billing staff. Provide comprehensive training to billing and collections teams on propensity to pay scores and how to communicate payment options empathetically.
- Agency management automation. Reduce the manual workload of audit agency transfers by automating the reconciliation process.
- Monitoring patient accounts. Look for a solution that regularly checks for changes or updates in patient ability to pay or contact information.
- Performance tracking. Monitor KPIs to adjust collections over time and improve forecasting.
How Experian Health solutions support better groups
Changing long-standing collection practices is often a major investment. However, the cost of inaction is often greater. Experian Health Collections Optimization Manager It uses propensity-to-pay models, driven by machine learning, and data-driven workflows to help healthcare providers improve patient populations. Our industry-leading comprehensive solution provides a smarter, faster way to collect patient payments, and Experian Health’s experienced advisors are there every step of the way, as your collection needs change.
Learn more about how Experian Health is data-driven The ideal solution for patient groups Helps revenue cycle management staff collect more patient credits.
(tags for translation) Collections Optimization (R) Collections Optimization Manager (R) Health Cone (R) Healthcare Groups (R) Medicaid (R) Medicare (R) New Health (R) Patient Connection (R) Patient Text (R) Revenue Cycle (R) Revenue Cycle Management (R) Weill Cornell (R) Worcester Community Hospital
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