Everyone who works in any number of industries knows the positive impact technology has on increasing efficiency. There’s no reason to waste time doing redundant work, and, in some instances, computers are much faster at performing tasks—like compiling and analyzing data—than humans. Today, we have an abundance of data which can be used for a variety of purposes. Predictive analytics is one use of technology that’s improving life for clinicians, patients, and those working in revenue cycle management.
It makes sense to separate the work between what’s best done by technology and what’s best done by humans. In this article, we’ll explain the role predictive analytics can play in helping determine the charity score for patients.
Predictive Analytics in Healthcare
Predictive analytics uses data from the past to make predictions about the future. It often utilizes statistical modeling, data mining techniques and/or machine learning to find patterns in data.
Here are two examples of how this is being used on the patient-side of healthcare. First, predictive analytics have detected early signs of patient deterioration in the ICU, where rapid decision making is essential. A second example is how it combines data from a variety of sources to identify at-risk patients at home to prevent readmission to the hospital.
In addition, predictive analytics has been used to forecast patient demand and staffing needs, which has increased both staff satisfaction and nursing retention. Since staff satisfaction has been shown to correlate with patient satisfaction, it’s a win-win for hospitals.
Predictive Analytics for Early Out Clients
There are a number of reasons patients might not pay their medical bills on time. If the reason is because of their ranking on the federal poverty guidelines, it could be a waste of time contacting them for payment. They simply might not have the funds.
Thanks to predictive analytics, a charity score can be generated, that can then be used to write-off an amount before it would be sent to bad debt, and go through the costly collection process. By using this data, health systems can determine tiers of charity that should be available to their patients.
This score is a five digit number. The first two digits are based on the federal poverty guidelines and the remaining three digits are the person’s probability/propensity to pay.
In order to evaluate a patient’s stability, ability to pay and willingness to repay debt obligations, the following information is evaluated:
- Source Record Activity
- Identity Verification
- Identify High Risk Conditions
- Residential Address History
- Educational and Occupational History
- Assets and Evident of Wealth
- Derogatory Public Records
Once all this information is captured, simple modeling can be completed based on charity policies. At this point, the options include applying to charity, writing-off the account or continuing to assess the account.
All of this takes place without contacting credit bureaus, which prevents unnecessary complaints from patients.
An Additional Path to Payment
When talking to patients, our patient account representatives are always asking questions to remove roadblocks to payment. Sometimes that leads to discovering insurance coverage. Other times it means qualifying patients for financial assistance.
Using predictive analytics to determine which patients are highly qualified for charity application, is yet another way we help our clients obtain payment.
We’re proud of the long-term relationships we have with our clients and are committed to improving the revenue cycle for our clients. If you are interested in learning more about our First Party Accounts Receivable Management Please contact us.