Member diagnosis codes are an increasingly critical data point used to calculate payments in risk adjusted payment models, measure quality, and for organizations to better understand the populations they serve.

For Medicare Advantage health plans the consequences of inaccurate diagnosis codes are significant. Poor documentation practices can lead to a health plan being under-reimbursed for that member, negative findings in Risk Adjustment Data Validation (RADV) audits, and the appearance of quality measure gaps that have already been closed.

SCIOClarity Chart Validation is a retrospective strategy wherein SCIO’s experienced staff coordinates the retrieval and auditing of member medical records from provider groups to maximize diagnosis code accuracy.

The service is used by health plans as a part of their overall efforts to ensure optimal performance in their risk adjustment and quality programs.

Common Diagnosis Code Documentation Errors:

  • Mistranslating physician notes to ICD-10 codes
  • System limitations on the number of diagnosis codes that can be included
  • Codes dropped during transmission between stakeholders

THE FIVE ELEMENTS OF CHART VALIDATION

1 SUSPECT LIST CREATION
SUSPECT LIST CREATION
Auditing medical records for each member is neither realistic nor cost effective. In this step, SCIO consultants pair their expertise with actionable insights powered the SCIOClarity Risk Adjustment and Quality Analytics platform to generate lists of members most likely to have incorrect or missing diagnosis codes. Multiple factors are considered in this calculation, including: prior risk adjustment factor scores, provider coding history, potential underlying data drop off issues, and more.
2 PRIMARY CARE PROVIDER ENGAGEMENT
PRIMARY CARE PROVIDER ENGAGEMENT
Effective documentation and submission of diagnosis data often hinges on a positive working relationship with providers. SCIO has contacted providers on behalf of health plans since our founding and understand the need to minimize abrasion during these interactions. In this step, our representatives professionally and respectfully connect with providers through phone calls and letters to arrange document retrieval. Because we are using the records only to validate diagnosis coding, providers rarely complain about our requests.
3 MEDICAL RECORD ACQUISITION
MEDICAL RECORD ACQUISITION
Providers have multiple ways to send documents to SCIO: regular mail, fax, or digitally. This flexibility allows providers to choose whichever method is most convenient for them. Additionally, to further reduce any abrasion, we coordinate the timing of their document submission to avoid any unnecessary resource drain on their offices.
4 CHART REVIEW AND CODING
CHART REVIEW AND CODING
Once the medical records are received, SCIO’s team of CPC- and HCC-certified coders completes a thorough review of these materials against the member’s existing diagnosis codes. During this analysis SCIO’s coders either capture missing diagnosis codes or, if codes are present, validate whether they are supported by physician notes and make corrections if necessary.
5 REPORTING
REPORTING
All findings are reported by SCIO in the client’s preferred file format so they may be easily re-submitted to CMS, NCQA, and/or other oversight organizations.

Impact to Risk Adjustment Programs

This service improves risk adjustment programs in two separate and critical ways. First, when the service captures previously missing diagnosis codes that map to HCCs, the organization will benefit from more accurate risk adjustment factor (RAF) scores to be used in the CMS-HCC Risk Adjustment Model which also may lead to higher capitation rates. Second, when the service validates or corrects existing diagnosis codes, the organization can be confident in their ability to perform well in Risk Adjustment Data Validation (RADV) audits.

Impact to Quality Programs

With complete documentation of diagnosis codes, organizations can be assured that the numerators and denominators used to calculate care gaps in HEDIS, Stars, and other quality measures are an actual reflection of their efforts. Organizations with internal population health analytics additionally benefit from a more robust and true data set that improves their understanding of their members and are better prepared to support higher quality care at lower costs.