5 Steps to Automating Credit Balance Resolution

By Eric J. Boggs, Jamshid Ebadi, and Ryan W. Hartman, MHSM, CHFP
| 2/19/2019
Experience working with Crowe clients indicates that the average revenue cycle employee takes 15 minutes to research and resolve just one credit balance account using conventional manual methods. That adds up to just 32 accounts per eight-hour day, or only 160 accounts per workweek. The typical medium-size hospital’s patient accounting system creates thousands of credit balances per month. With one team member resolving only 32 accounts per day, it becomes difficult to resolve the daily churn of new credit balances, much less work an existing backlog of accounts.

Credit balance management is an often overlooked aspect of the revenue cycle, despite the potential risks associated with neglect, including missed payment opportunities as well as the financial penalties associated with unmet regulatory and managed care requirements. To help streamline their credit balance resolution process and make better use of valuable staff time, organizations should consider using technologies to enhance and automate their reviews and resolutions.

Why automate?
Machine learning algorithms allow automated systems to learn how to resolve credit balances by examining prior resolution activity, identifying patterns in the data, and using those patterns to predict how best to resolve an account.

This technology can help an organization resolve credit balances faster. It also frees up team members so they can focus more time on addressing accounts with complex decision points, including those account resolutions that require follow-up with payers.

Steps to automation
Here are five steps an organization can take leading up to automating credit balance resolution through the use of technologies such as machine learning.

1. Identify resolutions to which technology can be applied. Accounts that are monotonous and simple and that don’t require much human judgment to resolve are good candidates for automation. Examples include correcting contractual adjustments or resolving credit balances created when a patient payment is made after a bad debt adjustment.

2. Determine the best resolution processes. Leadership should establish an organization’s performance standards by thoroughly reviewing existing processes and determining which ones are yielding the best results.

For example, an organization might have a team of 10 people who are resolving credit balances. Some team members likely are working more effectively than others. Leaders should identify those high-performing team members and determine what processes they are following to resolve credit balances. At the same time, leaders should identify and exclude any underperforming processes from the team’s workflow.

To help identify best practices and performers within their credit balance resolution teams, leaders can ask questions such as:
  • What is the preferred outcome for each type of credit balance, and which performers are achieving that outcome?
  • How are team members informed about changes to regulatory guidelines for resolving credit balances?
  • How are team members verifying that they are adhering to contractual requirements in place with payers?
After completing this analysis, team leaders can begin to transition to the next step – standardization.

3. Standardize best practices for credit balance resolution. Armed with a comprehensive understanding of how to work credit balance accounts effectively and efficiently, leaders can begin to agree on a set of ideal processes for their organization to follow. These best-practice processes should be included in organizational documentation regarding credit balance resolution and communicated to all staff and any technical resources with which the organization works. To help standardize best practices for resolving credit balances, an organization can do the following:
  • Make sure all staff are educated about the processes.
  • Institute defined reporting and tracking of staff performance.
  • Align performance management related to resolving credit balances with overall performance management metrics.
  • Define a process for making sure all staff members are following the same guidelines and using the same processes.
4. Systematize resolution best practices. Once an organization has identified the most efficient processes for resolving credit balances and instituted them as standard policies, it can move forward with integrating these processes throughout the entire revenue cycle system. Then, it can move toward using more advanced technologies, such as machine learning, to further improve processes.

5. Automate work steps within credit balance resolution. Finally, as an organization more proficiently incorporates technologies into its credit balance resolution workflows, it can begin to fully and confidently automate certain credit balance resolutions. As already noted, ideal types of accounts to automate are those that are tedious and do not require a human touch, but automation also can be extended to other types of credit balances such as patient refunds and insurance refunds.

Look to outside resources
Resolving credit balances can be a costly and time-consuming activity. Automation is one way to improve workflow and make better use of valuable human resources.

Many organizations may not have the in-house technology or staff resources available to take advantage of the full benefits technology can bring to the credit balance resolution process. Healthcare organizations should consider engaging with third-party specialists who can take a comprehensive look at patient accounts, transition the organization to a more technology-based credit balance management process, and, ultimately, improve credit balance resolution while reducing organizational risk.
 

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Eric Boggs
Eric J. Boggs
Principal
people
Jamshid Ebadi
Principal
people
Ryan Hartman