Bringing the revenue cycle to the next level
It’s the new norm in healthcare – using data to drive clinical decisions and achieve better outcomes. So, why are so few healthcare organizations taking the same approach and using financial data and analytics to support a stronger revenue cycle?
With revenue stakes at an all-time high and patients shouldering more payment responsibility, most healthcare organizations are missing a significant opportunity to effectively leverage critical data that can mitigate bad debt or delayed payment risk. In fact, most healthcare organizations already have vast amounts of financial data to work with; they simply don’t know how to use it effectively to achieve optimal financial performance now and in the future.
It’s no different than the move to data-driven clinical decisions and population health management. Organizations should be taking this type of approach to financial performance from the moment the patient makes the appointment to the time final payment is received.
Consider these three scenarios that demonstrate how a data-driven approach paired with the right analytics limits financial risk and improves the bottom line when it comes to patient payments.
Scenario 1: The Right Coverage
When registering for an inpatient procedure, a patient – Mr. Smith for the sake of this example – indicates he has no insurance. Instead of accepting Mr. Smith’s self-pay status at face value, the hospital turns to data, analyzing his financial information and determining if he qualifies for Medicaid, health insurance exchange subsidies or tax credits, the hospital’s charity care program or other financial assistance programs. After examining the data, the hospital determines Mr. Smith is actually eligible for Medicaid, and the registrar begins the enrollment process.
By reclassifying self-pay, uninsured or underinsured patients as soon as possible in the patient experience, both the patient and the organization benefit. Patients see a significant reduction in their portion of the bill, improving the likelihood of payment. Using tools supported by data and analytics to identify the right coverage also saves time and costs for the organization by eliminating unnecessary statements and collections phone calls because the patient is accurately classified much earlier in the revenue cycle.
Scenario 2: A Realistic Payment Plan
A patient faces a surgical procedure that likely requires two to three days of hospitalization. Although, Ms. Doe (in this scenario) has healthcare coverage, it carries high out-of-pocket and deductible amounts that impact her ability to pay. Recognizing the situation, the healthcare organization uses financial data to develop an optimal payment plan based on Ms. Doe’s unique financial situation, rather than giving her a generic payment plan that may not be affordable. As a result, Ms. Doe’s concern over paying the bill is reduced because of the tailored approach, and the procedure is scheduled.