What To Do When An AI Scribe Gets It Wrong

An error in a clinical note is serious, but it doesn't mean the technology has failed. It means your crucial role as the “clinician‑in‑the‑loop” has begun.

The efficiency of an AI medical scribe for clinicians isn't measured by its perfection, but by how seamlessly you can identify, correct, and learn from its inaccuracies. This guide provides a clear, actionable protocol to do just that— ensuring your AI tool remains a powerful asset for your practice, enhancing both your workflow and patient care.

I. Identify & Verify AI Scribe Errors

Before any correction, you must spot the error. Cultivate a habit of active, informed review. This is your role as the “clinician-in-the-loop”. The AI processes sound and language patterns; you provide the clinical understanding.

Evaluate These Common Error Categories:

• Homophones & Medical Jargon: The AI hears sounds, not meaning.
  • Example: AI transcribes “The patient is hypertensive,” when you actually said “The patient is hypotensive”. This single-letter auditory difference completely inverts the clinical status and required action.
• Negation & Context: Missed “no”, “denies”, or “rule out’ phrases can reverse the clinical picture.
• Medication & Dosage: “5mg” vs. “50mg” is a high-risk error. Always verify drug names and strengths.
• Anatomy & Laterality: “left” vs. “right” confusions are common and consequential.

Your Action: Treat the AI draft as a preliminary report from a junior colleague, essential, but requiring your expert verification before it becomes part of the legal record.

II. Correct & Train: The Feedback Loop

Once an error is identified, your response should do two things: fix the immediate record and train the AI for the future.

Execute The Correction Efficiently

Choose the fastest and best method for the error type:

• For Direct Edits: Simply click and type the correction into the draft. Best for minor typos or single-word changes.
• For Voice-Activated Correction: Use specific voice commands to edit without hands:
  • Example: Say, “Correct ‘no history of heart disease’ to ‘significant history of CAD in father”.
  • Why it's Technical: This method often provides a stronger signal-to-noise data for the AI’s Natural Language Understanding (NLU) model, directly associating your verbal correction with the original error.

Provide Structured Feedback

This is the most critical step for system improvement. Dont just fix the error, report it.

• Use the In-App Tools: Click the thumbs down or report button. This is not a complaint; it is you generating labeled training data (a set of curated examples that shows the AI the correct output for any given input).
• The Technical Impact: When you flag an error, you create a data point that tells the machine learning model: ‘The audio input for this segment should have mapped to this specific text output, not the one you generated’. This data is used to retrain and fine-tune the model, directly improving its performance.

By consistently correcting and flagging, you are not just a user of the AI; you are an active participant in its development, shaping it to better understand your voice and your clinical practice.

III. Optimize & Prevent Future Errors

Prevention is a highly effective form of error management. By optimizing your input, you engineer higher accuracy from the start.

Master the Audio Environment

Think of your audio as data streaming into the AI’s processor. Clean data yields clean output.

• Hardware Matters: A noise-canceling microphone isn't a luxury; it's a necessity. It filters out ambient “noise” (hallway chatter, construction, etc.) from the “signal” (your voice).
• Speech Discipline: Enunciate clearly and maintain a steady pace. Minimize crosstalk; when you and the patient speak simultaneously, the AI receives jumbled data and must guess.
• Technical Leverage: Use speaker diarization to your advantage. A clear, opening statement like “Okay, Sarah, what brings you in today?” helps the AI lock onto and separate the patient's voice channel from yours, structuring the entire note more accurately.

Structure Your Narrative

The AI excels at parsing complete sentences with clear context. Structure your dictation accordingly.

From Fragments to Formalism:

• Inefficient Input: “Lungs: clear. Heart: RRR. Abdomen: soft, NT.” (Requires AI to infer full meaning).
• AI-Optimized Input: “On pulmonary exam, lungs are clear to auscultation bilaterally. Cardiac exam reveals a regular rate and rhythm. The abdomen is soft and non-tender.”
• Signal your Intent: Explicitly state section transitions. “My assessment is …. And the plan is as follows…” This acts as a command, telling the AI to format the subsequent information under the correct heading.

IV. Know the Limits of the AI Scribe

Clinical judgement includes knowing when a tool's limitations outweigh its benefits.

Identify Systemic vs. Occasional Errors

A once‑off error is a data point. A repeating pattern is a system flaw. If the AI consistently struggles with a specific term (e.g., “Tocilizumab”) or concept, this indicates a gap in its training dataset. Document and report this pattern to your vendor; it's invaluable for their model retraining.

Recognize High-Risk Scenarios

In complex cases, such as a patient with overlapping comorbidities, the cognitive overhead of verifying an AI‑generated narrative can exceed the efficiency gained. In these moments, the most strategic decision is to disengage and use templated notes or direct dictation. This isn't a failure; it's an expert judgment call to prioritize accuracy.

Conclusion

An AI medical scribe for clinicians is not an autonomous practitioner. It is a powerful, if imperfect, instrument. Its ultimate value is not determined by its standalone accuracy, but by how effectively you integrate it into your workflow.

By adopting this protocol: Identification, Correction, Optimization, and Disengagement, you elevate the tool. You become the essential algorithm that ensures quality and safety, transforming the AI from a simple transcriptionist into a true clinical partner. This synergy ultimately frees you to focus more deeply on the patient in front of you.