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Risk Adjustment

Many payer and provider organizations are challenged with comprehensively and accurately identifying and documenting risk-adjusted conditions. Accurate risk adjustment leads to more appropriate preventative care initiated to ensure the most cost-efficient care can be delivered.

  1. The Growing Medicare Challenge
  2. The Core of Risk Adjustment
  3. Clinical Documentation and MEAT
  4. Healthcare NLP for Risk Adjustment
  5. Improving Coding Accuracy
  6. Risk Adjustment Case Study
  7. Human in the loop (HITL) AI for Risk Adjustment
  8. Deploying Risk Adjustment Workflows
  9. Beyond Medicare Risk Adjustment

The Challenge of Risk Adjustment

Many payer and provider organizations are challenged with comprehensively and accurately identifying and documenting risk-adjusted conditions. It is essential that healthcare organizations capture a complete picture of their patients in order to predict risk and outcomes accurately, to deliver effective and appropriate care. 

Inaccurate or inconsistent documentation and coding can leave healthcare organizations exposed to much higher levels of financial risk. By accurately capturing the comorbid conditions of members, with less manual effort, organizations can ensure they receive appropriate reimbursement to provide the necessary care to their members. 

The Core of Risk Adjustment

HCC codes are at the heart of the risk adjustment process: 

  • ICD-10 (diagnostic) codes are mapped to HCC codes to assign each patient's risk adjustment factor score; 

  • A risk adjustment factor score (RAF score) refers to a medical risk adjustment model employed by the Centers for Medicare & Medicaid Services (CMS) to represent the status of a patient's health. The RAF score allows payers and providers to identify potential patient complications and provides them with a more complete picture of each patient; 

  • With a better understanding of patients and their members, healthcare organizations can anticipate future financial requirements and predict the appropriate reimbursement. 

    Clinical Documentation and MEAT

    To support an HCC, clinical documentation is required to substantiate the presence of a disease or condition (appearance on a problem list in not sufficient). This is in addition to including the clinical provider’s assessment and management plan for the disease or condition.

    Most healthcare organizations use the Monitoring, Evaluation, Assessment, Treatment (MEAT) criteria as their documentation standard, together with the ICD-10-CM diagnosis coding and HCC assignments.

    Healthcare NLP for Risk Adjustment

    Linguamatics Natural Language Processing (NLP) enables payers and providers to connect technology with subject matter expertise in an easy-to-use workflow. This accessible approach ensures a higher level of confidence in an organization's risk adjustment submissions.  

    Our NLP platform also enables organizations to transform how they identify risk adjustable comorbid diagnoses by providing automated and semi-automated disease coding. 

    Our NLP also supports population health and risk stratification with publication grade accuracy. In both risk adjustment and precision medicine, our NLP platform has been proven to uncover diagnoses with > 90% precision and recall, and to significantly reduce medical chart review time. 

    Improve Coding Accuracy

    Linguamatics NLP can help organizations improve their coding accuracy: 

    • Automatically map clinician documentation to ICD10-CM and HCC  

    • Easily navigate to supporting documentation which is categorized as per the MEAT methodology  

    • Understand clinical context by distinguishing between negation, family history, synonyms and abbreviations 

      Risk Adjustment Case Study

      A large US healthcare payer serving ~200K Medicare Advantage members wanted to move to a more efficient automated and digital system. The diagram below illustrates how they used NLP to maintain risk scores for family members, and to submit reimbursement claims to CMS. 

      Using Linguamatics NLP they were able to improve medical chart processing by 25 – 50%, helping nurses identify conditions they might have previously missed. This payer is using NLP in other areas including analyzing transcribed call center notes to look for conditions such as colon and breast cancer, and in population health, assessing social determinants of health such as loneliness and lack of mobility. This company considers NLP a core capability with the payer community.

      Risk adjustment case study - Existing Payer Customer

      Increase Accuracy of Risk Adjustment

      Our interactive interface puts facts into the hands of experts. 

      Having combed through lengthy medical records (often hundreds of pages long), our solution identifies conditions, codes, and risk categories and presents these to the coder to accept or reject. 

      With the medical record presented in an easy to consume format (see fig. 1), coders can also highlight additional text and add new codes that may have been missed – or alter NLP presented codes to ones of their choice. This is where expert and technology combine in true augmented intelligence.

      HART risk adjustment HITL

      Figure 1: Each review consists of a left-hand panel of individual results to curate, alongside a large view of the complete document with color-coded highlights

      With each code linked to associated text in the medical record, highlighted and presented to the user, an audit trail exists to back up each code submitted. If coders disagree with suggested codes, a note taking function lets them capture their reasoning should this be challenged in the future.

      Deploying Risk Adjustment Workflows

      Not every organization has the same requirements when it comes to deployment – therefore, our solution is available in a variety of deployment models. With the integrated functionality of the NLP Data Factory, volume is not an issue, and our NLP risk adjustment workflows can be configured to our customer’s needs. 

      Because healthcare data is complex and often messy, there are a suite of pre-processors to deal with the different sources and formats in which payers receive data: Optical Character Recognition (OCR) correction for scanned PDFs, region detection for Consolidated Clinical Document Architecture (CCDAs) or Health Level Seven (HL7), we have the tools to ensure that the highest accuracy results are obtained.

      Beyond Risk Adjustment

      NLP can help healthcare organizations beyond risk adjustment: 

      • extracting member level social determinants of health (SDoH) data 

      • providing additional features for predictive models 

      • sentiment analysis in call center transcripts 

      • mining broader datasets such as FDA drug labels to gather information on reported adverse events. 

      Our NLP platform offers flexibility across multiple use cases, allowing organizations to adopt an enterprise strategy for unstructured data. 

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