At a Glance
Hospitals can improve their ability to collect on patient accounts by tailoring collection efforts to four segments of patients:
- Segment A: Patients with a high likelihood of payment and low account balances
- Segment B: Patients who have high original balances, but also a high likelihood of payment
- Segment C: Patients who have large balances and low expected collection rates
- Segment D: Patients with low balances and low expected collection rates
The self-pay population is not as simple as it once was. Gone are the days when accounts in this segment automatically meant "indigent" or "charity care." Today, many types of consumers are self-pay patients, including insured patients who are paying balances due on their hospital bills after insurance.
Unfortunately, most hospital collection processes continue to treat all self-pay patients the same. In doing so, hospitals miss opportunities to collect from willing individuals or age accounts unnecessarily.
One indicator that your self-pay collections need attention is if you are mailing the same letter to all self-pay patients, regardless of their balance or ability to pay. This process may be easy, but certainly it isn't effective or in the best interest of the organization.
The following best practices for segmenting self-pays have been validated by several leading healthcare networks.
Success Starts at Registration
The most successful hospitals focus first on using technology to screen their accounts at registration, then use established policies and procedures to guide their self-pay patient interactions. The following steps help organizations classify their self-pay accounts to receive the highest payment with the lowest account aging.
Step one: Screen for government program eligibility. Patients may not be aware of state and/or federally funded programs designed to assist them. As a service to patients, hospitals should assist patients in identifying appropriate programs that match their financial status. This step not only helps patients minimize billing concerns, but also offers your organization some level of payment that wouldn't be available if the accounts were classified as charity care or bad debt. Using a third-party solution specializing in Medicaid processing, organizations should screen all self-pay accounts at registration.
Step two: Screen for charity program eligibility. Hospitals are continually challenged to fulfill their mission, and charity screening helps ensure that patients who qualify for charity programs benefit from them. To identify patients who should be offered discounts based on your organization's charity care policy, all accounts that could have a balance after insurance, as well as all self-pay accounts, should be automatically screened for charity care eligibility at registration. Once eligibility is identified, a sliding scale for charity discounts should be employed.
By classifying charity accounts early in the cycle, you avoid sending them to collections that will deliver low rates of return or writing them off to bad debt. It is better for your financial performance, not-for-profit status (if applicable), and reputation in the community to enroll these patients in your charity program.
Step three: Screen for payment likelihood. Hospitals should use automated tools to identify the likelihood that a patient will pay his or her bill, and how timely the patient's payments are likely to be. At registration, all self-pay accounts should be assessed; registrars should then use these findings to direct the patient through a tailored admittance process that fits their likelihood-to-pay status. This unique process should be well scripted and should clearly identify the next steps for the patient, using the following approach:
- High likelihood of payment: Registrars should follow the traditional admitting process. As the return on these accounts is very high, hospitals should aim to make the patient's experience as smooth as possible to encourage repeat visits.
- Medium likelihood of payment: Registrars should ask for a moderate down payment for services from these accounts, and refer patients to a financial counselor to review payment plan options to increase likelihood of payment.
- Low likelihood of payment. A minimum down payment should be received prior to receiving services, and a financial counselor should review payment plan options with patients.
Charting Your Self-Pay Population
The first step toward a smarter self-pay process is to have accounts receivable (A/R) managers segment the self-pay patient population based on two variables: expected collection rate and original balance. The original balance is often most easily retrieved from the hospital information system (HIS). However, measuring or assigning an expected collection rate (or payment likelihood prediction) to a patient is not quite as easy.
Assessing Expected Collection Rates
To determine the expected collection rate for patients, most hospitals look at past payment history, if available, or better yet, leverage a service that provides patient credit scores and other payment predictor information. Many factors can impact a patient's likelihood to pay or the expected collection rate, so a comprehensive view of the patient's demographics and credit history is important. The following are just a few examples of how data can help hospitals more accurately predict payment.
Accurate demographics. Patient records include incomplete or inaccurate address information more than 25 percent of the time, and it is not uncommon to see hospitals with 70 percent or higher. Accounts can age significantly if caught in the return mail loop. By validating the accuracy of this information at the point of service, hospitals can greatly improve collections. After all, patients cannot pay a bill they never receive.
Marital status. If a patient is part of a dual-income family, the likelihood of payment increases. Even if the patient is out of work due to illness or injury, chances are the spouse will remain in the workforce.
Employment status. A patient's employment status will greatly impact the hospital's ability to collect. If a patient is currently unemployed, expect delays.
Medical credit score. Research has shown that medical bills are often paid after patients first pay their mortgages, credit cards, and other bills. A traditional credit score may be not be the most useful. Instead, hospitals should access a patient's medical credit score to better understand the priority the patient places on paying medical expenses in a timely manner.
Facility historical collectability. For repeat customers, hospitals can often predict payment likelihood based on past performance. If balances and collection times remain constant over time, the hospital may be fairly confident of similar experiences for current and future bills. To retain repeat customers who promptly pay their bills, many hospitals are asking their registrars to verbally express the hospital's gratitude. Often a thank you goes a long way.
Emergency admits. Unforeseen trauma visits are accounts that often are the most difficult to collect. In almost all cases, patients have not had an opportunity to plan how to manage their financial commitment for these events.
Balance after insurance confusion/delays. A patient's financial liability may be determined by his or her insurance. Thus, the patient may wait to pay the hospital until all insurance processing is completed-delaying collection on his or her portion of the bill. Hospitals have found that patient education is essential on this point. Misunderstandings regarding billings not only negatively impact a hospital's bottom line, but also are the greatest component of patient dissatisfaction.
Using Predictive Modeling
Predictive modeling involves the use of automated tools to assess likelihood of payment. Predictive modeling also enables hospitals to catch fraudulent claims by identifying patients who submit incorrect information at registration. This is done by comparing the provided financial information provided by the patient with data received from third-party providers and/or credit bureaus.
Hospitals such as Parkland Hospital of Dallas, which provides indigent care to residents of Dallas County, Texas, use predictive modeling software to forecast likelihood of payment while identifying those who are trying to abuse the system. The hospital has investigated more than 140 patients who are suspected of having lied about their financial status and place of residence in order to receive free care from the hospital.
Any predictive modeling tool used must be calibrated to best fit the organization. The process typically begins with a review of patient accounts that are 12 to 24 months old. Three consecutive months of account outcomes data are reviewed to create a statistically valid analysis. This information is combined with credit and financial data to predict the likelihood of payment, with results classified as "high likelihood," "medium likelihood," or "low likelihood" of payment. Patients are then segmented into categories based on their likelihood of payment.
The results allow hospital billing departments to use different approaches for patients with high versus low probability of payment. For example, Segment A-accounts for patients with a high likelihood of payment who have low account balances-typically will require minimal effort to collect. Most of these patients will respond in a timely manner to a simple letter reminding them of their payment obligations. An even better solution is to keep these accounts from even reaching a collection stage. To do so, many hospitals are now having their registrars simply ask for payment at point of service, thus eliminating any collections or aging.
Segment B-patients who have high original balances, but also have high medical credit scores, good historical collectability, and a strong employment status-also are highly likely to pay. The objective with this group is to minimize account aging. Although higher balances may require a greater collection effort than is required for patients in the first segment, the financial benefit will likely justify this effort. Hospitals should be more aggressive with this segment to prevent account aging, and should quickly resort to outbound call campaigns.
Patients in Segment C have large balances and low expected collection rates. Often, their balances result from emergency admissions; frequently, they are uninsured. A/R managers should spend time evaluating the cost to collect these accounts and be smart about the resources they apply. For example, if much of the original balance has already been paid by insurance, are extensive collections efforts going to cost more than recovering the remaining 20 percent of the bill? Consider your efforts, especially those using costly human resources, carefully.
The last segment, Segment D, comprises patients with low balances and low expected collection rates. First, the account should be screened to see if the patient qualifies for your charity care program or state or federal government programs. If not, the account should be immediately sent to a third-party collection agency. No internal resources should be spent on noncharity accounts in this segment; if they are, they should be highly automated. The better solution is for the registrar at the POS to request payment before care is given, if possible.
Final Strategies for Success
Once segmentation of accounts has been performed, hospitals should develop strategies to streamline and improve self-pay, starting with their communications.
Improved communications. Too often, collection letters or bills are printed in mass and include confusing language, acronyms or codes for medical procedures, and superfluous information. If a bill cannot be easily understood (e.g., what service is it for, has insurance paid its portion, current balance due), it will not be paid. All communications via mail, e-mail, or phone need to provide clear, concise information. It sounds easy, but too often hospitals fail this simple step.
New payment options. Plastic surgery centers, Lasik providers, and dentists have relied on payment plans or financing options for their patients for years. Most often, this option is managed by a third party that offers credit cards or payment plans that can only be used to pay for medical or dental services. Many hospitals are considering implementing these plans to alleviate many payment and collection activities.
Show appreciation. Repeat patients are used to hearing from you when bills are not paid, but what about those patients who are responsible and pay their bills on time? Hospitals can and should show these patients their appreciation, even in simple ways. For example, when a registrar checks in the patient and sees a history of on-time payments, the registrar could simply say, "Thank you, Mr. Smith, for paying your hospital bills so promptly. We really appreciate it." In today's world, it is amazing what a simple "thank you" can do.
Tina Eller is a senior revenue cycle strategist, SearchAmerica, Maple Grove, Minn. (email@example.com).
Publication Date: Sunday, June 01, 2008