The question isn’t “Should we use AI?”—it’s “How do we select the right one?”
To ensure a sound and forward-looking selection, AI-powered HCC tools must be evaluated through critical considerations and strategic inquiries.
For risk adjustment teams operating within complex clinical and regulatory environments, success hinges on explainable, scalable, and reliable technology. Payers and provider organizations are expected to do more with less, faster, more accurately, and at scale.
The demand is certainly for more than a tool—it's for a trusted partner.
Every HCC captured (or missed) impacts revenue, compliance, care quality, and credibility downstream. Moreover, with value-based care becoming the new normal, it presents a challenge for risk adjustment decision-makers to buy into the AI promise, which requires more than just shiny demos and slick sales decks.
Instead, it calls for critical thinking, challenging questions, and a guided approach.
Here’s what risk adjustment leaders should consider:
1. Start with the problem and not the vendor and its technology stack
Good AI isn’t about replacing people—it’s about empowering them. One of the most critical selection criteria points to the painstakingly slow and inherently flawed manual chart review process (e.g., the OIG has observed a 12-15 percent variance in coding results and quality).
AI, when applied and used correctly, should reduce variance, introduce reasoning and explainability, while preserving and improving consistency and coding quality.
If a vendor can’t show how their tool lightens that load, without compromising compliance, they are simply a coding service. They will carry the burden of coding variance and associated risks.
2. OCR isn’t just a checkbox
Optical Character Recognition (OCR) converts images or scanned documents, like printed text, tables, or forms, into searchable, editable digital text. Yet, to a seasoned expert or “student of the game,” it's clear OCR often struggles with tables and structured layouts—issues that usually go unnoticed. Low-fidelity OCR or tools not built for forms lead to errors that harm conversion, NLP coherence, coding, and audits. Always verify vendor claims; OCR accuracy in context truly matters. Choosing the right technology—one that truly understands both structured and unstructured data—is not just smart; it's essential. Seek solutions that convert forms and fables with context, explainability, auditability. OCR is not as effective as advertised.
Don’t just ask, “What OCR engine powers the platform—proprietary or off-the-shelf?” Also ask, “How does poor OCR impact downstream coding accuracy and coder productivity?” And crucially, request proof of the actual output that the coder sees.
This isn’t a complex exercise; share three to five golden PDFs to blind test and validate vendor claims. Also, request per-page conversion costs to assess the true ROI of the technology accurately.
3. Coders don't hallucinate—context keeps them grounded, and neuro-symbolic AI is catching up
Conventional systems rely on basic keyword spotting or rigid rules, often missing clinical nuance, complex patterns, or faltering under data volume. Best-in-class AI, built on narrow language models and now enhanced with neuro-symbolic AI, converts unstructured data into structured, machine-understandable formats with fidelity. It provides contextual understanding, identifies supporting evidence, and explains code suggestions. Evaluating such tools demands a ‘trust but verify’ mindset, through simple tests and validation of accuracy and auditability.
Ask:
- Can AI code suggestions be explained with evidence, or is validation burdening coders, increasing risk?
- Has the vendor explained which AI categorization their solution fits, according to NIST and CMS guidelines?
Importantly, this aligns with the White House's "Blueprint for an AI Bill of Rights," which advocates for transparent, explainable systems—especially in sensitive domains like health care, where rights, access, and outcomes are at stake.
4. Deployment should fit you—Not the other way around
Some organizations want an end-to-end platform. Others want to plug AI directly into existing workflows. (e.g., Epic, internal portals) A modern HCC solution should offer both UI User Interface (UI) and Application Programming Interface (API). Vendors should be asked how their technology accommodates cross-operational flexibility, allowing the integration with existing systems, and is the core technology 3rd party, open-source, proprietary, and/or otherwise ‘white-labeling to explore and surface-associated risks and scalability.
5. Accuracy you can trust — and audit
Compliance in risk adjustment is high-stakes. Auditability, transparency, and traceability must be built into the AI’s DNA.
- Can the system explain its code selections?
- Does it support coder feedback loops and retraining?
- Does the dashboard show confidence scores or only final results?
Trustworthy AI reveals its logic, not just outcomes
These principles are echoed in the AI Bill of Rights, emphasizing independent evaluation, transparency, and accountability in algorithmic systems.
Policy watch: The White House's "Blueprint for an AI Bill of Rights" offers five principles for responsible AI, emphasizing transparency, equity, data privacy, and human-centered design. As risk adjustment evolves, these principles provide a valuable lens to evaluate the trustworthiness of AI partners. RAAPID adopts this forward-thinking approach through its AI architecture, built on neuro-symbolic and socument AI.
This ethos is affirmed by the AI Bill of Rights, which emphasizes the right to a human fallback, clear explanations, and protection from discrimination and unsafe technology. These principles reinforce RAAPID's design choices and commitment to coder-first, clinician-aware AI.
6. Workflow integration isn’t optional
Real-time insights are not limited to coders; they are leveraged in care management, quality initiatives, and payment integrity. Integration should be seamless, with the tool designed to operate within existing dashboards and electronic health records (EHRs) without disrupting workflows.
7. Don’t forget the humans behind the tech
Lastly, what's more important is that AI is only as good as the people and processes supporting it. Domain expertise matters. Risk adjustment isn’t a plug-and-play business. You need a partner who understands the stakes—who’s walked in your shoes, not just coded your software.
Final thought
If your AI vendor doesn’t help you ask better questions, it might not have the best answers.
Whether current capabilities are under review, a future system is being sourced through an RFP, or responses are being evaluated, the need for a guided methodology to choose the ideal AI-powered HCC coding solution remains paramount.
RAI has immense potential to transform HCC coding, but choosing the right solution isn’t just a tech decision—it’s a strategic one. Read beyond the buzzwords. Ask hard questions.
And most importantly, choose a partner who sees your success as shared.
About RAAPID
RAAPID specializes in AI-powered risk adjustment solutions that help health care organizations accurately capture patient risk through advanced DocumentAI and Neuro-Symbolic Clinical AI technology. RAAPID's HITRUST-certified platform transforms unstructured medical data into actionable insights, allowing payers and providers to improve coding accuracy, reduce physician burden, and optimize appropriate revenue in value-based care arrangements. Following a Series A investment from M12, Microsoft's venture fund, RAAPID continues to expand its impact across Medicare Advantage, ACA, Medicare ACO, and Medicaid programs.