Approx. 1,050 words

Introduction

Your team has already solved one of the most painful parts of outpatient medicine: getting clean, structured data before the visit. Yet there is still a stubborn leak in the value chain. Even when the chart is perfectly pre-populated, physicians often under-document or mis-select Evaluation and Management (E M) codes—leaving revenue on the table and inviting payer scrutiny. The good news: you are in a unique position to fix this next mile with minimal lift.

In this article we will explore:

1. Why the E M coding problem persists despite well-designed intake workflows
2. How lightweight AI interventions can train clinicians in the moment
3. A pilot blueprint that keeps capital burn low while proving ROI
4. What a design-partner collaboration with Revedy could look like

1. The Hidden Revenue Gap After a Perfect Intake

Your automation platform already gathers history, ROS, and demographic data long before the encounter. Providers love opening a chart that is half written. However, two silent failure modes remain:

• Compression to Level 3: Time-pressed clinicians default to 99213/99203 simply to stay on schedule, even when documentation supports a higher level.
• Documentation/E M Mismatch: The pre-chart note may have the necessary elements, but the final assessment omits medical-decision complexity language, breaking the link between work performed and code selected.

Both scenarios undercut the ROI story you deliver to practices. Worse, payers now use increasingly sophisticated analytics to spot patterns of miscoding, turning under-coding into an audit risk if the documentation level and code level disagree.

2. Why Traditional Coding Solutions Don’t Fit Your Model

Typical computer-assisted coding (CAC) platforms focus on hospital inpatient notes or require deep integration into the billing system—heavy engineering and long sales cycles you have wisely avoided. Your footprint sits before the EHR, so you need something that:

• Runs as an API-first microservice you can call during pre-chart generation
• Handles both E M selection and compliance hints in plain language for the provider
• Learns quickly from a modest volume of outpatient encounters

That is precisely the gap Revedy’s agentic AI coders address. Our architecture was built for specialty use cases like intra-operative neuromonitoring, where edge cases are the norm and data volume per client is small. The same traits make it ideal for an intake automation company looking to add coding guidance without heavyweight infrastructure.

3. Three Quick Wins to Train and Coach Providers

Below are practical interventions you could embed in your existing workflow with minimal UI changes.

a) Inline “Coding Confidence” Badges

When the pre-chart is generated, a Revedy endpoint can return:

• Proposed E M level (e.g., 99214)
• Confidence score (0–100)
• Two missing documentation elements if the confidence is under 85

Render this as a simple colored badge—green for high confidence, amber for “needs one more bullet,” red for under-documentation. Because it appears before the physician starts writing, it becomes a just-in-time training tool rather than a retrospective denial.

b) Structured Feedback Loop After Signing

Once the visit is closed, send the signed note to the same endpoint. If the final E M level differs from what the AI predicted, post a short Slack-style message to the clinician:

Yesterday’s follow-up visit for patient AB: predicted 99214, submitted 99213. Missing elements: chronic condition status, medication management detail.

Physicians receive concrete, case-specific coaching while the encounter is fresh in memory—far more effective than monthly billing reports.

c) Low-Friction Audit Packets for Payers

Because your platform already captures every intake answer, you can automatically bundle supporting documentation with the claim. Revedy’s coding engine tags each line it used (history, ROS, MDM) when selecting the code, so generating an audit packet is trivial. This turns potential payer push-back into a fast “see attached” response, protecting both you and the practice.

4. Pilot Blueprint: Capital-Efficient and Fast

Revedy was founded by engineers and clinicians who understand early-stage constraints. Our typical proof-of-concept follows four steps and requires no long-term commitment.

| Week | Activity | Your Engineering Lift |
|——|———-|———————–|
| 1 | Map your existing FHIR or custom JSON intake schema to Revedy’s coding endpoint (one afternoon) | 2–3 hrs |
| 2 | Send 50 historical encounters through the API; compare predicted vs. actual codes | Zero UI work |
| 3 | Toggle the inline badge in a staging environment; capture provider feedback | Minor front-end tweak |
| 4 | Go-live for one specialty, max 3 providers, for 30 days | Feature flag only |

Because our platform is API-driven and HIPAA-ready out of the box, security review is straightforward. We typically execute a Business Associate Agreement at the start of week 2.

5. What a Design-Partner Relationship Looks Like

Our early customers gain more than software—they shape the roadmap. Concretely:

• Priority Feature Requests: If your physicians need, say, split-shared visit detection or 2025 E M guideline updates, they go to the top of our backlog.
• Joint Validation Studies: We co-author a white-paper-style analysis (no PHI) demonstrating documentation improvement. You can share it with prospects or investors.
• Revenue-Share Flexibility: For young companies that prefer opex neutrality, we can structure pricing as a percentage of incremental collections validated by third-party RCM data.

6. Practical Scenario Walk-Through

Imagine a busy ENT clinic using your intake product for all new-patient visits. A 55-year-old with chronic sinusitis fills out the ROS and history at home; the pre-chart is generated.

1. Before the visit: The Revedy badge shows amber—predicted 99204 but confidence 80 percent because medical decision complexity is unclear. A tooltip reminds the physician to document prior unsuccessful therapies.
2. During dictation: The physician adds a sentence on previous antibiotic courses. Badge turns green; predicted 99204, confidence 95.
3. After signing: The physician actually bills 99203. Slack alert explains the downgrade and lists the extra work documented, nudging the clinician to correct the claim or adjust future behavior.

Over time, the ENT group sees fewer downgrades from payers and tangibly higher revenue per visit—without more clicks or longer notes.

Conclusion

Your platform already lightens the administrative load pre-encounter. By extending that same philosophy to E M coding accuracy, you can deliver a full Intake-to-Revenue value proposition. Revedy’s API-first, specialty-aware AI coders make this extension possible with almost no engineering risk and zero change to physician workflow.

Next Steps

1. Identify one high-volume specialty to pilot—family medicine, ENT, or rheumatology work well.
2. Schedule a 30-minute technical scoping call with Revedy’s CTO team.
3. Kick off a four-week proof-of-concept and measure uplift.

A small experiment today can unlock a durable competitive advantage for your automation suite tomorrow. Let’s make every perfectly prepared chart translate into perfectly captured revenue.