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Use this checklist to evaluate AI tools for medical coding and documentation compliance.
If you're running a practice on eClinicalWorks and looking for an AI medical coding tool, it’s important to start from the understanding that not every tool that describes itself as "AI-powered" works the same way. Many are just sophisticated rules engines now rebranded as AI.
The best way to select a tool is to forget about the AI label and focus on the capabilities and outcomes. Asking the right questions will help you tell the difference between tools that protect revenue integrity and guarantee clinical compliance from tools that just add steps to your clinical documentation workflow without delivering meaningful gains.
What the eClinicalWorks EHR includes
eClinicalWorks already includes a built-in coding engine. The Clinical Rules Engine (CRE) helps providers save time by automating code assignment based on documentation triggers. For example, when a provider enters a lab value with results in a certain range, or a certain diagnosis or vital sign that triggers a rule, the system fires a pre-configured rule and drops the corresponding CPT or ICD-10 code into the note. Their AI-powered RCM tools also include progress note analysis for E&M recommendations, claim scrubbing, and a claims rules engine that learns from payer rejections.
That's a solid foundation with multiple patches for fixing leaks across the revenue cycle. But rules-based systems have real limits that an external AI coding tool can close.
Rules-based logic vs. LLM reasoning: The core distinction
Rules-based engines use structured logic to yield deterministic outcomes. When paired with natural language processing capabilities (NLP), these engines can sometimes parse text for keywords for detecting applicable diagnosis and procedure codes.
The CRE has both of these capabilities: It reads structured data inputs—things like a BMI value, a blood pressure reading that gets entered into specific fields—and matches them against ranges of pre-set criteria to determine the right diagnosis codes. It also uses NLP to identify CPT coding opportunities.
What it can’t do is analyze the entirety of the provider’s clinical documentation—including handwritten notes and faxes—to identify missing codes, flag a documentation gap that creates payer audit risk, or evaluate whether the level of E/M coding is actually defensible against what the note says. These are qualities that will determine if a claim will survive an audit or get paid on the first pass.
In comparison, tools that use large language models (LLMs) can read the full clinical narrative the way a human auditor would and make qualitative determinations about whether the documentation supports the codes being billed.
7 criteria to evaluate when choosing a tool for eClinicalWorks
1. Can the integration be customized for your eCW workflows?
Workflow disruption can easily kill the ROI that vendors promise your team. If your staff is copying codes between systems, you're already losing the efficiency gains.
In healthcare, it pays to find a vendor that will customize your implementation to fit your workflows. When it comes to AI coding and broader chart review functions, this means the AI reads notes directly from eClinicalWorks as soon as a provider closes it, then pushes corrected or validated codes back into the billing workflow. It does all of this without requiring providers, coders, or billers to leave the platform.
Customized integration also means your team gets to decide which decisions the system can make on its own, and which require human-in-the-loop review.
2. Does it go beyond what eCW’s CRE already does?
If all a tool offers is rules-based code suggestion from structured data, you’d be paying for something eClinicalWorks already provides. The value of an external AI tool lies in what the built-in engine can't do: reading free-text narratives, validating that documentation actually supports the codes being submitted, identifying undercoded encounters, and collecting metrics for automated provider performance feedback and care delivery standards for clinical quality.
3. Does it include checks for clinical documentation improvement (CDI)?
A strong AI coding tool shouldn't be a point solution. Traditional compliance rules engines can usually only evaluate whether documentation is present—not whether it actually satisfies payer-specific guidelines or your internal policies for clinical documentation integrity.
An LLM-based tool can catch documentation deficiencies that open compliance gaps, flag notes that fail to meet payer standards, and generate automated provider feedback before a claim ever goes out the door.
For eClinicalWorks practices, this is especially relevant because the platform's native claim scrubbing checks for code validity but not documentation sufficiency.
4. Does it replace manual, sample review with 100% coverage?
Many practices still audit a sample of charts for coding accuracy. Sometimes the sample is random and viewed as representative. At other practices, rules-based engines sideline certain charts (such as those submitted by new providers or that contain high-risk CPT codes) for manual review. The reason they do this indicates one of their weaknesses: rules-based systems like those built into most EHRs are useful for prioritizing which charts should get more thorough review by a human. But they’re not conducting the same kind of thorough, multi-leveled review a human would conduct.
By definition, these workflows for exceptional or higher-level review mean most of your revenue leak goes undetected. Using AI for automated and detailed analysis of 100% of your encounters uncovers every undercoded visit and underleveled E/M code. For one primary care practice, AI chart review uncovered that 12% of its notes were undercoded. By using AI to detect and correct those notes, the practice achieved 5x ROI upon implementation.
Confirm that any tool you evaluate reviews every note, not just flagged exceptions.
5. Is coding and compliance review payer-specific?
Your payer mix matters. Medicare, Medicaid, and commercial insurers have different documentation requirements for the same codes. A tool that applies generic coding rules across all payers will miss denials that a payer-specific engine would catch. Ask whether the tool can apply different compliance logic by payer, and whether it flags claims at risk of denial based on specific payer rules before submission.
Charta's revenue cycle team page frames this as the difference between capturing revenue you've earned and leaving it on the table due to preventable denials.
6. Does the tool maintain a defensible audit trail?
Any AI that touches medical codes must be HIPAA-compliant and generate a detailed audit trail showing which codes were suggested, which were modified, by whom, and when. This isn't optional: it's the baseline for audit preparedness. To be worth the investment, an AI coding tool should shift your practice from risk mitigation to risk avoidance, with every AI decision backed by documentation citations.
7. Does the tool guarantee outcomes?
This is the vendor question most practices forget to ask. AI for its own sake has no value.
Ask vendors whether they guarantee specific outcomes, such as clawback protection, denial rate reductions, or guaranteed ROI. Vendors who stand behind their technology won’t flinch.
Comparison: eClinicalWorks CRE vs. comprehensive AI chart review
The table below compares eClinicalWorks' native coding capabilities against what a dedicated LLM-based AI coding tool (such as Charta Health) adds to the workflow.
Bottom line
eClinicalWorks gives you a solid rules-based coding foundation. The Clinical Rules Engine handles repetitive validation of codes from structured data, and the newer AI RCM features add real value in claims scrubbing and denial learning.
But if your practice wants to close the gap between care delivered and revenue collected—especially through undercoding detection, documentation compliance, and full pre-billing coverage—a dedicated LLM-based tool that integrates directly into eCW is the upgrade worth evaluating.