The Data-Driven Path to More Efficient Claims Adjudication

Unlocking Operational Efficiency with Data Standardization and Interoperability

Introduction

As healthcare organizations continue their transitions from volume-based to value-based payment models, they are becoming more reliant upon a set of curated data to support critical business functions.

The driver of these business functions are claims. These claims must flow through a number of processes and systems (some manual) to eventually pass adjudication and continue on to the provider actually being paid.

The problem? The applications and systems around these claims are performed manually or are outdated, prone to failure, and inhibitive to an integrated data architecture. To migrate from legacy or purely manual processes is an operationally intensive task and requires the appropriate technology and expertise to complete.

The Central Role of Data

Let’s talk about the claim: what is it? It’s a piece of data that represents a transaction. A transaction, in this case, that describes what happened in your encounter with your healthcare practitioner(s). If you were diagnosed with a condition or had a procedure performed, this information is on the claim submitted to your health plan so that the practitioner can get paid (and so you only pay what you have to) based on the contractual rates.

So, we’ve got a claim. But how did we get it? It starts in the doctor’s office and makes its way to your health plan. Then what? It goes through this process called the claims adjudication process. This process is a series of steps that validates the claim in different ways:

• Is the doctor’s information correct?
• Is the patient information correct?
• Is the subscriber information correct?

These are only a few ways the claim is inspected. If the claim, for one reason or another, fails any of these validation steps, it gets rejected and the submitting practitioner needs to fix the claim (or worse, the claim has been rejected for lapse of coverage or non-authorization). When this occurs, all processes are delayed, and upon resubmission, the claim must go through the entire procedure once more.

If your claims adjudication process is built on legacy systems with no real integration capabilities, this can be an incredibly slow and arduous process which ultimately costs everyone involved: the patient, the practitioner, and the plan (it’s a lose-lose-lose). The alternative can reduce the number of human-related errors and expedite the validation process of a claim so that if there is a critical validation issue, it can be promptly reported back to the submitting party.

Enhancing Reimbursement Accuracy

The big problem with claims adjudication boils down to an organization’s ability to automatically adjudicate claims. If you need to manually inspect every claim, you’ll never get through them all (well, maybe, but it won’t be fast). So, it’s best that we automatically adjudicate as many claims as we can – we won’t get all of them, but every percent increase in auto-adjudication rates represents huge cuts in operational overhead.

This auto-adjudication process is built through the integration of a series of built-for-purpose systems within the enterprise architecture – each system in the processing chain inspects and performs activities with or against the claim; for instance, checking that the provider’s NPI is valid or that the taxonomy is valid. Each system also must have the capabilities that allow it to integrate with the next system down the chain. Any “re-keying” of data introduces a potential for error and bottlenecks. This complex chaining of systems can often create isolated data silos that lack in consistent standardization and data governance, further compounding the problem of interoperability.

When these processes come together in a way that delivers timely and accurate data, organizations can leverage a more accurate clinical data set to drive initiatives around population health management and HEDIS measurements. This more mature state of leveraging claim and clinical data enables organizations to (among other things) provide the best patient experience, improve health outcomes, and deliver health care services at a reasonable cost.

Challenges and Solutions: Barriers to Data Standardization.

Unfortunately, all too often an organization’s claims adjudication process is riddled with issues. Poor interoperability, lack of true standardization, unsupported legacy systems, and just outright fully manual processes are major barriers in the push towards higher adjudication rates – not just higher auto-adjudication rates – but even faster rates of manual adjudication.

These challenges are not insurmountable; however, each requires a strategy that utilizes a data management platform capable of effectively addressing them.

• Legacy system migrations need their data to be migrated according to the appropriate standardization and validation rules.
• Consolidation of data governance and standardization activities into your data management platform ensures all systems involved in the adjudication process adhere to the governance model.
• A comprehensive, pre-validated, and standardized data set can support the implementation of modern healthcare applications, filling operational gaps currently managed manually. This approach ensures a confident and successful launch of new systems.
  
  

How Gaine Helps / Claims Integration Hub

At Gaine, we have the platform and expertise required to successfully migrate your legacy claims adjudication process into your modern enterprise architecture. We have guided major health plans through the process of implementing the Coperor Health Data Management Platform. This journey has enabled them to uncover existing data issues and identify legacy systems suitable for migration. As a result, these organizations have significantly improved their auto-adjudication rates and enhanced real-time access to claim statuses.

Talk to a Gaine expert today to automate and streamline your organization’s claims processes.