Key takeaways:
- Healthcare organizations are facing growing levels of bad debt and a sharp decline in collections.
- Propensity-to-pay models that utilize machine learning and robust data offer insight into a patient’s likelihood to pay and allow staff to focus their collections efforts where they matter most.
- In 2024, Experian Health clients that implemented Collections Optimization Manager saw a 10:1 ROI. Some clients, like Weill Cornell Medicine, have seen up to $15 million in recoveries.
Healthcare organizations are facing a sharp decline in collections and an increase in bad debt. Rising self-pay costs and more patients struggling to afford their medical bills are contributing factors. Inefficient collections practices, reliance on third-party agencies that don’t utilize propensity to-pay scores and manual processes are also key contributors to this growing market problem. Providers who adopt propensity-to-pay models that use data and automation to forecast the likelihood of payment often see both improved revenue recovery and patient satisfaction.
Here’s what to know about propensity-to-pay collections strategies in healthcare.
Why propensity to pay matters in healthcare collections
“Propensity to pay” is a data-driven model that identifies patient populations with the greatest likelihood of paying, to enhance existing collection strategies. When billing teams better understand a patient’s propensity to pay, they can easily prioritize outreach and allocate collections resources effectively. This eases their workload, as they can focus their efforts where they’ll have the greatest impact, and on accounts with the highest probability of payment. Keeping more collections in-house also reduces the reliance on expensive third-party agencies, while eliminating wasted effort on low-yield tasks – like repeated phone calls or mailed statements to accounts unlikely to pay. The need to adopt propensity-to-pay models has grown in recent years as patient volumes and the cost of care continue to grow.
In the last 20 years, U.S. hospitals have absorbed nearly $745 billion in uncompensated care, according to American Hospital Association data.
American Hospital Association
Rising healthcare costs and the newly enacted “One Big Beautiful Bill Act” are expected to shift even more financial responsibility to both hospitals and patients.
Unfortunately, many organizations still rely on inefficient collections processes, third-party agencies and medical billing practices that lack propensity-to-pay insights. The result? Disruptions to the entire revenue cycle, including lost patient revenue, wasted resource hours, increased costs to collect, and high vendor costs. Using outdated collections strategies also contributes to patient dissatisfaction and churn, causing even more revenue leaks.
Why healthcare providers need propensity-to-pay analytics
Limited staff capacity and high volumes of self-pay accounts further compound collections challenges for organizations that have yet to adopt propensity-to-pay analytics. As collections timelines drag out, providers can be left with cash flow issues, revenue losses and bad debt.
This ultimately disrupts the revenue cycle and affects the quality of patient care – and the entire patient experience. By leveraging propensity-to-pay analytics, revenue cycle leaders can boost revenue cycle predictability and streamline collections efforts.
Listen in as Weill Cornell Medicine and Experian Health discuss how a smarter collections strategy 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.
How propensity-to-pay models work in practice
Propensity-to-pay models screen and segment patient accounts based on the likelihood of payment. Segmented accounts receive a propensity-to-pay score – from 1 to 5, with 1 being the highest likelihood to pay — and are then transferred to appropriate reconciliation channels.
Experian Health’s solution, Collections Optimization Manager, leverages machine learning, predictive analytics and data sources – like credit, behaviour and demographics – to identify which patient accounts have the highest likelihood to pay. It also automatically screens patient data for deceased, bankruptcy, Medicaid and charity.
Patient accounts are then sorted into pay groups through data-driven segmentation. This allows busy collections staff to quickly clean up accounts receivable and put their focus where it matters most – patient accounts with the strongest chance of paying their bill.
With a clear picture of a patient’s financial situation, healthcare organizations can improve patient communication and further boost collections efforts to maximize revenue. High-propensity accounts may receive light-touch reminders, like less frequent bill reminders. At the same time, alternative financial assistance, such as charity care or payment plans, can be made available automatically to low-propensity patients.
Benefits of using propensity-to-pay models
Propensity-to-pay models, like Experian Health’s Collections Optimization Manager solution, offer numerous benefits to organizations that strengthen the revenue cycle.
- Higher collections rates: Using a propensity-to-pay model makes AR more manageable, especially for high-patient-volume organizations. Complimentary tools, like Experian Health’s PatientDial and PatientText, easily send self-pay options via voice or text message, boosting patient engagement and building trust.
- Reduced bad debt: Propensity-to-pay models help identify patients with a low likelihood of paying their medical bills.
- Lower collections costs: Chasing payments on accounts that are deceased, bankrupt, or eligible for Medicaid or charity wastes valuable resources. With propensity-to-pay models, busy staff can efficiently work on high-yield accounts in-house, reducing the number of accounts that need to go to third-party vendors.
- Faster cash flow: Prioritize likely-to-pay patients early and shorten payment cycles, which can improve revenue cycle predictability.
Implementing propensity-to-pay analytics: Best practices
Healthcare organizations that implement propensity-to-pay analytics should consider the following best practices:
- Choose the right partner. Look for a technology partner, like Experian Health, with extensive data assets and healthcare expertise.
- Automate patient communication. Reduce overhead and increase collections efforts with automated patient communication strategies.
- Ensure alignment with legacy technology. For real-time accuracy, choose a solution that integrates seamlessly with existing EHR and billing systems.
- Train billing staff. Provide comprehensive training to billing and collections teams on propensity-to-pay scores and how to communicate payment options with empathy.
- Automate the agency management. Reduce the manual workload of auditing agency remittances by automating the reconciliation process.
- Monitoring patient accounts. Look for a solution that regularly scans for changes or updates in a patient’s ability to pay or contact information.
- Track performance. Monitor key performance indicators to fine-tune the collections process over time and improve forecasting.
How Experian Health’s solutions support better collections
Changing longstanding collections practices is often a significant investment. Yet, the cost of inaction is often greater. Experian Health’s Collections Optimization Manager uses propensity-to-pay models, driven by machine learning, and data-driven workflows to help healthcare providers improve patient collections. Our comprehensive industry-leading solution offers a smarter and faster way to collect patient payments, and Experian Health’s experienced consultants are there every step of the way, as collections needs shift.
Learn more about how Experian Health’s data-driven patient collections optimization solution helps revenue cycle management staff collect more patient balances.
