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.
By leveraging comprehensive financial information, such as patient responsibility estimates and payment history along with organizational guidelines, the organization can build a patient-specific payment plan. A tailored plan not only makes the patient comfortable, but also results in a plan that enables the organization to receive the most optimum payment – not too high for the patient, or too low for the healthcare organization. This front-end, data-driven strategy not only improves collections, but also enhances patient satisfaction and staff efficiency, yielding a higher propensity for patient payment and lowering default rates.
Scenario 3: Focused Collections Efforts
A hospital’s internal collections staff receives two patient accounts. One patient has a history of late payments and a sizeable bill, while the other has an excellent payment record and a smaller amount due. Previously, the collections staff would process both accounts in the same manner. Now, using a data-driven approach to collections that incorporates a custom segmentation code derived from payment history, credit data and other variables, the staff prioritizes collections efforts based on which patient has the greater propensity to pay.
This targeted use of data and analytics allows staff to work smarter with delinquent balances on the back end by determining the most productive accounts on which to focus collections efforts. In this example, despite one patient having a smaller bill, the data shows that collections staff should focus efforts on collecting from this individual based on payment history and other data points. Data-driven segmentation also can help determine which accounts should be routed to internal staff and which should be handled by an external collections agency. This type of focused collections strategy results in a more positive patient experience, lowered financial risk and an improved bottom line.
The Future Requires Actionable Data
The idea of using actionable data to move financial discussions with patients to the front-end of the revenue cycle while also supporting back-end collections may make some healthcare organizations uncomfortable. Yet, when viewed as a way to better meet patient needs while improving the organization’s current and future financial viability, it is easy to see the tangible benefits of this data-driven approach for all. Minimizing payment risk early in the patient encounter helps avoid delays in the payment cycle. Using data to identify risk and provide realistic solutions enhances both revenue and the patient experience. Driven by changes in the healthcare market, a data- and analytically-driven revenue cycle is the standard every healthcare organization should strive for and reach. The data is available, and now it’s time for organizations to leverage it.