Unlocking Hidden Revenue: Why Unstructured Data is Your Biggest Risk Adjustment Opportunity

In the world of risk adjustment, what you can’t see can absolutely hurt you. While your Electronic Health Record (EHR) does a great job of capturing structured data—the checkboxes, dropdowns, and billing codes—it often misses the most valuable clinical information. The true, detailed story of a patient's health is almost always buried in unstructured text: the narrative of a physician's progress note, the findings in a faxed specialist report, or the details in a hospital discharge summary.

Manually sifting through this mountain of text is a slow, expensive, and error-prone process. As a result, most organizations are leaving significant, legitimate revenue on the table and exposing themselves to compliance risks by failing to capture the complete and accurate risk profile of their patients.

The Goldmine Hidden in Plain Sight

Unstructured data represents the biggest untapped opportunity for improving Risk Adjustment Factor (RAF) scores. Here’s where the most critical details are often found:

• Physician Progress Notes: This is where a clinician's thought process lives. They describe the severity of a condition, link symptoms to diagnoses (e.g., "shortness of breath due to CHF exacerbation"), and detail treatment plans that satisfy MEAT criteria.
• Specialist Consult Reports: A report from a cardiologist or nephrologist often contains the most specific diagnoses (e.g., "diastolic congestive heart failure, stage III" instead of just "CHF") that carry higher HCC values. These reports frequently arrive as faxes or PDFs, disconnected from the primary EHR data.
• Hospital Discharge Summaries: These documents are a rich source for capturing acute conditions that have resolved but may have manifestations or sequelae that are still risk-adjustable.
• Lab and Imaging Reports: A radiologist's narrative in an imaging report can identify comorbidities or complications that aren't coded elsewhere.

The problem is clear: your coders are human. They can't possibly read every word of every document for every patient. They are forced to skim and search for keywords, a process that inevitably misses crucial context and nuance and leads to several common HCC coding errors.

How MedChartScan Helps You Find Value in Text

This is precisely the problem that AI was built to help solve. A clinically-trained Large Language Model (LLM) like the one that powers MedChartScan doesn't just search for keywords; it is trained to process and interpret clinical narratives with a level of speed and accuracy no human team can match on their own.

1. Comprehensive Data Ingestion: Our platform connects to all your data sources. It doesn't matter if the information is in an EHR note, a scanned PDF, or a faxed image. MedChartScan ingests and digitizes it all, creating a single, searchable patient record for your team.

2. Surfacing Clinical Context and Nuance: The AI is trained to differentiate between a current, active problem and a condition noted in family history. It is also trained to recognize clinical shorthand and surface potential links between symptoms and their underlying cause. For example, it can surface a potential link between "Type 2 DM" on page 1 and "Stage 3 CKD" on page 4, suggesting a review for Diabetic Chronic Kidney Disease (HCC 18 + HCC 137). You can see a detailed breakdown of this exact type of AI-assisted review process here.

3. Highlighting MEAT Evidence for Faster Validation: MedChartScan doesn't just flag a potential HCC. It highlights the exact sentence or phrase in the source document that may provide the MEAT (Monitored, Evaluated, Assessed, Treated) evidence. This makes the coder's validation process incredibly fast and helps create a human-validated, bulletproof audit trail.

4. Helping Coders Uncover Net-New Diagnoses: By performing the initial comprehensive review, our AI consistently helps coders uncover high-value HCCs that were never entered as structured data and would have been missed by manual review. This translates directly into more accurate RAF scores and appropriate reimbursement.

Stop letting your most valuable data remain locked away in unstructured text. By leveraging AI to assist in a comprehensive, intelligent review of all patient documentation, you can empower your team to ensure coding accuracy, capture the complete risk of your patient population, and unlock the revenue you have already earned.