Notes recorded in the EMR by transcription software during a visit need to be reviewed for accuracy and billing compliance, along with the many other daily processes. For a practice focused on providing quality care and growing the business these daily tasks as up fast.
The disconnected tools problem
The problem is not that the tools do not exist. It is that they are disconnected. EMRs and transcription software handle what they handle.
The clinician is the one bridging the gaps manually, every visit.
What we built
Using N8N, a workflow automation tool, hosted in a secure environment for compliance, we built an automated workflow that takes notes stored in an EMR, assess them based on per-defined crieteria and provides analytics to leadership.
- 01 TriggerVisit transcribed, charted, and stored in the EMRThe visit completion triggers the workflow automatically.
- 02 AICompare the notes to expected outcomesReviews the charts and assigned CPT codes and provides feedback
- 03 HumanProvider receives feedback and reviewsProvider is given data points to assist with reviewing and revising the encounter at their discretion
- 04 AnalysisWeekly reporting provided to leadershipLeadership receives aggregate reports to analyze trends and work back with staff
- 05 SendFeedback LoopThe expected outcome criteria can be revised over time to optimize the system
How the workflow runs
The clinician records their notes using transcription software integrated with the EMR during the visit. An AI model runs a review: looking at what CPT codes are relevant based on transcription and the visit data, like what codes were actually entered, start, and end time, etc, and provides feedback. The goal being to ensure accurate billing and reduce disputes with payors. The provider receives a summary of the review and uses there judgement on if changes are needed.
Everything gets stored in a database alongside the visit records.
Why the database matters
EMRs are good at recording medical records associated visit data. Transcription software is readily available these days. They are not set up to surface what was not billed, and cannot be customized to meet your clinicls needs. Furthermore, they are difficult to audit for accuracy. Over time, the visit database gives the practice a way to identify patterns: CPT codes that are consistently underused relative to the visit notes, documentation gaps that recur across specific clinicians or locations, trends that would otherwise stay invisible inside the EMR.
A note on the human in the loop
The workflow does not replace clinical judgment. Every AI output goes to the provider for review before it is acted on. The goal is to reduce the administrative burden, not to remove them from the process.
Given the stakes involved in healthcare documentation, that distinction matters.
The result
Early feedback provides an opportunity to adjust coding before the billing cycle progresses to the payer. Over time the feedback builds a story that can be analyzed and used as a data point for operational improvements
If you are running a scaling practice and this sounds familiar, we are happy to talk through what it would look like for your setup.
