How to Get Smarter About Encounter Data Error Management

How manual is your process to reconcile, and resolve errors in both RAPS / EDPS? Effective error management is critical for submitting full encounter data that will pass Center for Medicare and Medicaid Services CMS’s requirements.

Understanding the differences between RAPS and EDPS is hard enough. Meeting the May 1, 2017 deadline for data submission is creating an even bigger challenge for many Medicare Advantage plans.

Because of the volume of data involved, EDPS leaves a lot more room for errors. Effective error and workflow management, therefore, is critical if you hope to submit full encounter data that will pass the Center for Medicare and Medicaid Services’ (CMS) requirements.

Manage errors based on financial projections

At this point in the year, your MAO may have captured the data it will use for submission. From now until May, you need to detect errors in that data, resolve them and ensure that whatever submission goes to CMS is the best it can be. If you are using an automated system, you may have the ability to detect errors quickly. But what do those errors mean to your bottom line? Creating an error management system allows you to put your resources where they will deliver meaningful financial results.

Prioritize and resolve errors by category and type

You may believe you have to resolve all errors in your submission data. But doing so can keep you from reaching the finish line. Instead, consider the benefits of grouping errors by category and fixing them in order of the impact they will have on your RAF score.

This kind of error management cannot be accomplished manually in a matter of months. You can however, use an automated system to reach your goals. With the following features incorporated into your risk adjustment software program, you will have the tools you need to realize maximum reimbursements from your EDPS submissions.

  1. Error identification
    Finding errors in data is a standard feature of most risk adjustment software. But a list of errors, by itself, is not enough. The first step to good error management is being able to make sense of the data you have. You want to be able to sort and categorize data based on multiple criteria, including the top 5 Encounter data errors in 999, 277 and MAO-02.
  1. Financial Projection
    Being able to see the financial impact of every error will allow you to prioritize the order in which you’ll resolve them. Your program should be able to highlight the errors with greatest HCC impact and provide accurate financial projections for reimbursement based on the particular errors and/or groups of errors you resolve.
  1. Error Queuing
    Once you’ve agreed on where to start, your system should automatically queue errors for resolution and create workflows to help you fix them within the application user interface. Another feature to consider is the ability to queue them for submission or resubmission.

No matter what system you decide to use, whether your own home-grown solution or a vendor, the number one consideration in order to get smarter about Encounter data is ‘workflow’. There are a lot of manual processes involved when handling data in RAPS and EDPS. Automating this process should be a priority for every payer looking into running a more efficient risk adjustment program.

Another consideration for Encounter data management is the fact that traditionally data management was an IT job, but not anymore. In order for payers to see improvements across programs, data needs to be seen from a business and strategy point of view.

Read more about why CFOs, revenue integrity professionals, risk adjustment managers, financial analysts, and other professionals need to play an active role in the way their organization manages data: Healthcare + Big Data: Bridging the Gap

 

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