Over the past few years, patients have been paying more of their healthcare costs. In 2018, about 48% of workers with employer health plans had high-deductible plans, up from 17% almost ten years before. By 2016, insured patients spent about $5,277 out-of-pocket on healthcare each year. These changes affect healthcare providers a lot. More than half of U.S. patients say they delay paying medical bills because of high deductibles or confusion about their insurance statements. When payments are delayed, hospitals face higher unpaid bills and extra work. Over 50% of healthcare chief financial officers say collecting payments from patients is their biggest challenge.
Propensity to pay modeling helps by predicting how likely patients are to pay their bills. The model looks at patient details like their finances, payment history, insurance, and sometimes outside data like housing or census info. It groups patients into high, medium, or low likelihood to pay. This helps healthcare teams focus on the right patients.
For example, Cedar’s payment solutions use lots of past data from places like ABC Medical Center. They found that how a patient paid before predicts if they will pay in the future better than just looking at how much they owe. With this, hospitals can send the right messages, offer discounts, and make payment plans that fit each patient. This helps them collect more money and spend less.
Providers using this method get benefits like:
Many U.S. healthcare groups have seen good results using propensity to pay models with their collection efforts. Two examples show how it works:
Novant Health made $16 million more from hospital and doctor bills and got a 9.5 to 1 return on their investment. They focus on patients based on who can and will pay. By sending personalized messages and offering payment plans to those with bigger bills, they cut down on unpaid money and collected more efficiently.
Cone Health collected $14 million with a 6 to 1 return using tech that automates workflows plus propensity models. Automation saved over 3,000 staff hours by quickly spotting patients who were bankrupt or passed away, so they didn’t waste time trying to collect from them.
These groups also automated Medicaid sign-ups and charity care checks. This helped them follow new debt laws quickly and offer patients a kinder billing experience.
Old ways of collecting money treat all patients the same, but that doesn’t work well now. People’s financial situations are different. Propensity to pay modeling sorts patient accounts so healthcare teams can:
This sorting helps cut costs by not spending time on patients who won’t pay. Data from Experian Health shows that using many kinds of data—like credit info and income—helps recover 76% more money from patients likely to pay compared to using just payment history.
Sorting also lowers call center work. Automated dialing that uses these models can cut 20,000 calls per 100,000 accounts by only calling patients who will probably answer. This makes staff work better and stops patients from being annoyed by too many calls.
Good collections teams also use automation to spot cases that need special care, such as deceased patients or those who went bankrupt, and to update contact info automatically. This helps bring in more money and avoid wasting time on accounts that cannot be collected.
Revenue Cycle Management, or RCM, means all money-related steps in patient care, from scheduling to getting paid. RCM has three parts:
Propensity to pay modeling mostly helps in the back-end phase but also affects earlier steps.
Patients now pay about 23% of medical bills, which causes more problems for healthcare businesses like more denied claims, billing mistakes, and unpaid bills. Denial rates rose from about 10.15% in 2020 to nearly 12% in 2023. Causes include coding errors, bad paperwork, and rules that are hard to follow.
Automation and AI tools help fix these issues by making claims accurate, speeding up payments, and giving quick feedback to staff.
Front-end automation, like online forms and eligibility checks, reduces errors that cause claim denials later. Mid-cycle AI helps with proper coding to make sure bills match the care done.
At the back end, propensity to pay models help focus collection efforts on patients likely to pay and automate contacts for others. This helps manage the aging of accounts receivable, cut bad debt, and improve forecasting. Predictive analytics also help decide which denied claims are worth appealing.
Closing the gaps in healthcare revenue cycles depends more and more on artificial intelligence (AI) and automation. About 75% of healthcare leaders are already using or plan to use AI to cut costs and boost efficiency. For example, automating checks of patient eligibility saves $6.52 per patient transaction, adding up to $4 billion in savings each year.
Only about 14% of healthcare groups now use advanced AI-powered propensity to pay models. This means many have not yet started using these tools to increase income.
Machine learning looks at patient info, payment history, and insurance behavior to improve chances of predicting payment. These models get better as they process new data, so healthcare groups can change how they reach out to patients as needed.
Benefits of adding AI and automation to propensity to pay models include:
Using AI, machine learning, and automation together creates a smarter revenue cycle. This brings steady cash flow and helps healthcare groups stay financially healthy while giving patients the payment options that fit their needs.
When using propensity to pay models, healthcare providers must keep track of their results to be successful. They look at measures like:
These numbers help teams improve their methods, patient communication, and overall money management. Healthcare groups like Novant Health and Cone Health show how using data and automation together gives better results and saves staff time.
Even though propensity to pay modeling helps, there are challenges:
These challenges need careful planning and ongoing review.
Healthcare providers in the U.S. can improve their finances and patient care by using propensity to pay modeling in revenue cycle management. As patients pay more out-of-pocket, data-based methods help balance money goals with fair patient collections. AI and automation make these efforts smoother, helping medical providers adapt to changes in healthcare payment systems.
Propensity to pay modeling is an approach used to identify customer populations that have the highest likelihood to pay their bills. In healthcare, it helps predict patient payment behavior by analyzing multiple data sources, enabling targeted collection strategies.
With rising out-of-pocket medical costs and high deductible plans, providers face increased challenges in collecting payments. Optimizing patient payment strategies is essential for improving collection rates and managing healthcare finances effectively.
Factors include financial status, behavior patterns, visit history, and demographic information. Additionally, third-party data, such as real estate information, can provide insights into payment likelihood.
Machine learning analyzes historical patient data to identify characteristics that predict payment behavior. It enables segmentation of patients and the creation of customized outreach strategies to optimize collection rates.
CPT codes help predict the optimal timing for follow-ups with patients regarding unpaid bills. They indicate the type of procedure, which can inform the level of urgency for collection.
The steps include gathering extensive patient data, assigning importance scores to predictive variables, and conducting individualized outreach strategies to maximize collections and reduce costs.
Cedar inputs historical data specific to each healthcare provider to develop tailored propensity to pay models, incorporating unique patient populations and enhancing the accuracy of predictions.
Identifying demographics assists in understanding payment behavior trends. Tailored communication and strategies can then be directed toward specific patient groups, improving overall collection outcomes.
Past billing behavior often predicts future payment patterns more accurately than the total amount owed, highlighting the importance of targeted collection strategies to improve lifetime collections.
Using machine learning models for propensity to pay allows providers to access evolving insights, enabling ongoing adjustment of outreach strategies and collection tactics to enhance efficiency and effectiveness.
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