The original version of this blog appears on Libman Education’s website here and was published on March 16, 2026.
By the time most organizations reach the vendor conversation stage, they have already read the marketing materials. And unfortunately, accuracy claims are all high. Case studies are promising. Every vendor’s value proposition sounds the same: coverage, quality, and scalability for overburdened revenue cycle teams.
The real work starts when you move past curiosity and into serious evaluation.
At this stage, the goal is no longer to be impressed. It is to understand risk, fit, and operational reality. The right questions surface not just what a technology can do, but how it behaves in the messy, exception-filled world of the revenue cycle.
The first place I recommend starting is going back to our first article on how autonomous medical coding is defined (“Understanding the 3 Tiers of Medical Coding Automation”). Before anything else, ensure the vendor you’re speaking with is truly autonomous, meaning no human touch in the coding decision itself.
Let’s explore the ten questions you need to ask potential autonomous coding vendors and why asking these questions helps ensure the success of your AI system deployment.
If the answer is “all encounters,” that should raise a red flag. Autonomous coding engines learn similarly to how a new medical coder develops, working through each code set and medical specialty section by section. So if a vendor claims they can do it all from the start, they likely have people working in the background or significant quality checking happening behind the scenes.
Numbers close to 90% early on should raise concern. On average, around 8-12% of encounters on the first pull are incomplete (unsigned notes, missing attestations, incomplete documentation). Another 5-10% require a provider query due to ambiguous or conflicting documentation. That alone puts you at a minimum of 12-22% of encounters that cannot be autonomously coded, due to factors no technology can control. Realistic autonomous rate ranges at one year post go-live by encounter type look like this:
Dig Into How Accuracy Is Measured and Validated
“95% accuracy!” is easy to claim but much harder to prove.
If you search “how is coding accuracy measured,” AHIMA and AAPC point to two common approaches: code-over-code and case-over-case.
Some organizations also apply weighted scoring systems, giving heavier weight to primary diagnosis codes or E/M codes over modifiers. This adds another layer of complexity.
Is it code-over-code, encounter-by-encounter, or a weighted scoring system? If weighted, understand how those weights are applied. A system that weighs primary diagnoses heavily could produce a strong-looking accuracy number while still missing meaningful secondary codes.
This is a two-part question. First, for regulatory purposes: how often does the vendor update the engine for ICD-10 changes, CPT updates, NCCI edits, and other governing body updates? Second, for machine learning-based engines: what does the feedback loop look like, and what safeguards prevent hallucinations from creeping into your production coding?
Edge cases are where risk lives. Understanding the range of diagnoses and case mix the engine handles helps you estimate what will fall back to your coding team and gives you the assumptions you need to build realistic productivity and ROI projections.
Understand How Exceptions Are Handled in Real Workflows
No autonomous coding vendor handles 100% of encounters. And you don’t want exception management to become another bolt-on workflow layer on top of an already complex revenue cycle.
What types of exceptions does the engine produce? How are they labeled? What information comes back to the coder? What is the turnaround time, and where do encounters land in the workflow? Revenue cycle leaders have been through enough technology rollouts to know that encounters can disappear into black holes, only to surface months later as timely filing misses. Every detail of the exception workflow matters.
If exceptions sit outside existing coder workflows, you have an efficiency and adoption problem. Bolt-on tools create friction and downtime risk. The vendor should be able to show you how exception management fits into your current workflows, not alongside them.
Clarify the Role Human Coders Play During and After Implementation
Leaders need to plan resources appropriately and understand the real impact on their teams.
This matters for setting realistic timelines, accurate ROI calculations, and the change management conversations you will need to have with your team. Underestimating coder involvement during implementation is one of the most common planning mistakes organizations make.
Are improvements to the engine driven by your team’s feedback, by the vendor’s own development roadmap, or a combination? Who is responsible for improvements, what is the process, and what is the timeline? The answer tells you a lot about what kind of partnership you’re walking into.
Get Honest About What Implementation Actually Looks Like
Timelines and implementation effort directly affect ROI and organizational readiness.
Once you’ve worked through contracting (which is almost always the first delay in a health system environment), the next question is whether slowdowns come from your side or theirs. Internal IT resourcing, audit resourcing, and data access requirements are common internal blockers. On the vendor side, you want to understand what has historically caused problems, how those issues were resolved, and what they have changed as a result. Ask them: from your first go-live to your most recent, what have you learned and why?
Their answer tells you whether they’re getting better with each implementation or repeating the same patterns.
Establish How Success Is Measured After Go-Live
Ask how the vendor defines success once the system is live, and whether those metrics are built collaboratively with your team. The best partners align success to your organizational goals, not just their product benchmarks.
Choosing an autonomous medical coding vendor is not about finding the most advanced technology. It is about finding a partner who understands healthcare operations and workflows, respects coding expertise, and is honest about both limits and strengths.
The right questions shift the conversation from promises to proof.
Ready to talk to vendors (or even start evaluation)? Check out Nym’s autonomous coding education hub for helpful articles, downloadable worksheets, and more!