Third-Party Aggregators Explained
Data-sharing is the provision of data by a data holder to a third party with the data owner’s consent. It includes the reusing of data based on commercial and non-commercial data-sharing agreements.
Data-sharing incorporates a collection of practices, technologies, architecture, cultural elements, and legal frameworks that relate to digital transactions of any information sent between individuals or organizations.
Third-party aggregators are essential for data sharing because they serve as channels for secure data sharing and promote trust between participants. They are intermediate technological platforms that manage and transfer data flows between providers and consumers. One function of aggregators is to develop interoperability between participants.
The third-party aggregators involved in risk adjustment are:
- Risk Adjustment Processing System (RAPS)
- Encounter Data Processing System (EDPS)
- External Data Gathering Environment (EDGE)
What is Risk Adjustment Processing System (RAPS)?
RAPS is the Risk Adjustment Processing System through which risk adjustment data are processed. Medicare Advantage (MA) organizations submit data in five fields of the reporting system:
- Date of service from
- Date of service to
- Provider type
- Diagnosis code
- Patient identifier
This data is abstracted from claims submitted by providers and includes diagnoses captured during internal or vendor-contracted audits of medical records. After the data submitted by Medicare Advantage (MA) organizations passes the checks in the Front‐End Risk Adjustment System (FERAS), the data is sent to the CMS data center for RAPS processing.
RAPS performs complete editing of all detail records stored in the RAPS database.
- RAPS performs balancing checks to ensure complete files were received from FERAS before editing data as a precautionary measure.
- RAPS edits detailed record transactions, including HIC number, provider type, from date, through date, and diagnosis code.
- If the date of birth is submitted, RAPS also edits that field.
What is Encounter Data Processing System (EDPS)?
In 2008, the Centers for Medicare and Medicaid Services (CMS) began transitioning from RAPS data files to EDPS data files as the basis for Medicare Advantage patient risk scores. As of 2020, data must be submitted through the EDPS—a more comprehensive system requiring additional information to help improve the overall quality of patient care.
Under EDPS, unfiltered encounter data are submitted directly to CMS. CMS then applies the filtering logic to extract the valid diagnosis codes. Diagnoses are then used in the risk score calculation process.
| Data | RAPS | EDPS |
|---|---|---|
| Enrollment Duplicates Validaty of diagnoses codes Requires CPT codes to complete certain functions Must pass National Correct Coding Initiative edits |
|
|
What is External Data Gathering Environment (EDGE)?
An EDGE server is a piece of hardware that performs data computation at the end (or “edge”) of a network. An edge server can provide calculations, networking, and storage functions like a regular server.
EDGE servers process data physically close to the end-users and on-site apps, so these devices process requests faster than a centralized server. Instead of sending unprocessed data to and from a data center, EDGE servers process raw data and return content to client machines. As a result, EDGE servers perform quicker, with lower latency and short loading times.
CMS requires that insurance companies maintain an EDGE server for risk adjustment. Data on the EDGE servers are used to ultimately calculate the RA transfer payments.
For insurance companies to perform quality assurance on EDGE submissions and to have a more transparent process to troubleshoot, CMS provides EDGE DIY software, which allows insurance companies to calculate their risk scores separately from the normal EDGE process.
Conclusion
Risk adjustment is crucial in ensuring providers are compensated appropriately for accurately documenting and reporting high-risk patient treatment plans. It is a statistical method for predicting a person’s likely use and costs of health care services. These third-party aggregators help to support data collection and sharing.
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