By Abhinav Aggarwal, Edifecs
Health plans participating in government programs are reimbursed in financial payments based on the encounter data they submit. Simple enough, right? A provider submits a claim to the health plan, the health plan verifies service and payments are made. Unfortunately, there’s more to it.
An encounter is a reported record of a claim that has been processed. It provides context about the service that was delivered to the covered member. In addition to facilitating payments, the encounter data is used to monitor the quality of service, service utilization, and compliance with contracted arrangements with the government. But wait, there’s STILL more! In order to receive proper reimbursement and have the appropriate oversight, the encounter data needs to be accepted! In order to be accepted, the encounter must adhere to the strict guidelines enforced by the government body (Medicare Advantage, the Affordable Care Act’s state exchanges or Medicaid).
The encounter life cycle is considered the “last mile” of the process. Generating and sending the encounter data downstream is inaccurately considered a “piece of cake.” So when the payments received don’t align with what the CFO was expecting or what performance ratings indicate, health plans tend to refocus on their encounter processing best practices – taking the “last mile” for granted.
Managing encounter data can be a nightmare. There are many factors that impact encounter data quality. Claims and encounters are processed from different sources and there are different claim types (professional, institutional, vision, dental, pharmacy) – each with its own set of business and compliance rules, and varying file formats (837 vs. NCPDP). Health plans must accurately track all claims received and processed from the time the claims are converted to encounters and submitted and to the time they are accepted by the downstream system. Any misstep along the process can lead to heavy financial repercussions.
The end-to-end claim to encounter life cycle can look like this:
- The claim starts at the provider’s office. The visit is documented and generated as an electronic healthcare claim (837 file)
- The provider either sends the claim directly to the health plan or through a clearinghouse
- The clearinghouse validates the claim and routes it to the health plan organization
- The health plan runs claims through its gateway for validation, orchestration, and transformation and sends it to downstream systems for processing
- The claims are adjudicated for payment processing
- Post adjudicated claims are sent to a data warehouse
- Claims are extracted and sent for processing as an encounter
- Encounters are then validated and transformed for submission and reconciliation with the downstream systems
At a high level, if everything goes right, this can be a seamless process. However, varying complexities often arise. For example, if the health plan doesn’t have end-to-end visibility on the entire claim-to-encounter-life cycle it won’t know if the data is being held for review. Or, occasionally data makes its way to that “last mile” and can take a significant amount of time to analyze and correct or approve.
There are multiple steps between the time a claim comes in the door and the time it is paid and is reported to CMS/State and gets accepted. Often, plans focus on claims that are rejected by CMS/State and miss the claims that came in the door but never even made it to the encounter system – leaving a significant amount of money on the table. To achieve successful encounter submissions and reconciliation in the government space, plans must track a claim end-to-end – from the time it comes in the door to the time it is accepted by CMS/State – to realize 100 percent of opportunity.
About The Author
Abhinav Aggarwal is the Senior Director of Encounter Submission and Risk Adjustment Solutions at Edifecs, a global health IT company. Aggarwal is responsible for working directly with health plans of all sizes to design health information technology solutions that meet plans’ data management needs and reduce the risk of costly penalties.