Reviewing AI-Generated Notes: The Basics

Not yet.

That note was generated by a language model. The same kind of technology we discussed in Module 1 — a word prediction engine that generates fluent, structured, confident text based on patterns. Most of the time, it is accurate. Sometimes it is not. And the errors, as you already know, do not look like errors.

Reviewing an AI-generated clinical note is not the same as proofreading a letter. It is a clinical act. It requires a specific approach, and it requires you to know what you are looking for. In this lesson, I am going to give you a systematic method for reviewing AI notes that is thorough without being slow.

Every AI-generated note needs four things checked. In this order. Every time.

First: structure. Does the note follow your practice’s expected format? Most AI tools generate notes in a SOAP structure — Subjective, Objective, Assessment, Plan — or a similar clinical template. Check that the sections are present, that the content is in the right section, and that the note reads as a coherent clinical record. If your practice uses a different format, you may need to rearrange the content.

Second: clinical accuracy. This is the core check. Does the history match what the patient actually told you? Are the examination findings correct — or were they even included? Is the assessment reasonable? Does the plan reflect what you actually discussed?

Third: safety-netting. Did the AI include appropriate safety-netting advice? Was the patient told when to come back, what symptoms to watch for, and when to seek urgent help? This is a legal and clinical requirement, and its absence from the note is a significant omission.

Fourth: the plan. Does the documented plan match the plan you agreed with the patient? Are prescriptions correct? Are investigations ordered? Are follow-up arrangements in place? Is anything documented that you did not actually agree to do?

Here is the simplest way to think about reviewing an AI note. Read the note and ask yourself: does this match what happened in the consultation?

Not “does this look like a reasonable note.” Not “would this be acceptable if I had written it.” But specifically: does this match what happened?

Let me give you an example of why the distinction matters.

You see a 55-year-old man with a two-week history of epigastric pain. You take a history, examine his abdomen, and find tenderness in the epigastrium but no guarding or rebound. You decide to trial a proton pump inhibitor and review in four weeks. You safety-net for red flag symptoms.

The AI generates a note that includes all of this. But it also includes: “No weight loss. No dysphagia. No change in bowel habit.”

Did you actually ask about those things? If you did, the note is accurate. If you did not — if the AI assumed you would have asked about red flags and documented them as negative even though the conversation did not cover them — then the note contains fabricated negatives.

AI notes are almost always incomplete. The question is what they are missing and how much it matters.

Examination findings are the most common omission. If you examined the patient but did not narrate your findings aloud, the AI has no way of knowing what you found. You need to add these manually. Heart sounds. Respiratory examination. Abdominal palpation. Skin inspection. Whatever you did with your hands and eyes, document it.

Clinical reasoning is usually absent. The AI documents what was said, but not why you made the decisions you made. If you considered a diagnosis and rejected it, the note will not reflect that unless you said it during the consultation. Adding a brief line — “Considered cardiac cause, but pain is postprandial and relieved by antacids, no cardiac risk factors” — demonstrates your clinical thinking and protects you if the case is reviewed later.

Nuance and context often get lost in the AI’s summarisation. The patient who was anxious about cancer but did not say the word. The social circumstances that influenced your management plan. The conversation you had about the patient’s preferences. These human elements of the consultation rarely make it into the AI note.

Sometimes the AI includes things that should not be in the note.

Hallucinated content — details that were not discussed but the AI included because they are statistically likely in the clinical context. The patient did not mention family history, but the AI added “no family history of bowel cancer” because it is a common element in notes about abdominal pain.

Misheard words — the AI transcribed “ten days” as “ten years,” or heard “paracetamol” when the patient said “Pantoprazole.” Audio transcription errors are common, especially with medication names, and they can have direct clinical consequences.

Over-interpretation — the patient described intermittent discomfort and the AI documented “severe pain.” The patient said they were “a bit worried” and the AI documented “significant anxiety.” These subtle exaggerations change the clinical picture.

Every one of these needs to be caught and corrected before you sign off. The note is not the AI’s note. It is yours.

There are three common approaches to reviewing AI notes, and each has trade-offs.

After each consultation — you review the note before calling the next patient. This is the most thorough approach because the consultation is fresh in your mind. You will catch more errors. The trade-off is that it adds 30 to 90 seconds per patient, which can feel significant in a busy surgery.

At the end of the surgery session — you review all notes in a batch. This is faster in some ways because you are not switching between consultation mode and review mode. But the consultations are less fresh, and you are more likely to miss subtle errors or omissions.

The next day — this is the riskiest approach. Memory fades quickly. By the next morning, you may not remember whether the patient said “ten days” or “two weeks,” and you cannot verify the AI’s version against your own recollection.

Let me address the practical question. How long does this actually take?

In my experience, and in the published literature from NHS practices using these tools, reviewing an AI-generated note takes 30 to 90 seconds per consultation. The variation depends on the complexity of the consultation, the quality of the AI output, and how much you need to add or change.

For a straightforward acute presentation — a sore throat, a urinary tract infection, a medication review — the note is usually accurate and complete. A quick read, confirm it matches, add your examination findings, sign off. Thirty seconds.

For a complex consultation — multiple problems, mental health, safeguarding concerns, medication changes — the review takes longer because there is more to check and more the AI might have missed or misinterpreted. Sixty to ninety seconds.

Compare that to typing the note yourself. Even a fast typist takes two to three minutes for a simple consultation and five to ten minutes for a complex one. The net time saving is real, even with thorough review.

But here is the critical point. That time saving only works if you actually do the review. Clicking “accept” without reading is not a time saving. It is a risk transfer — from you now to you later, when a problem surfaces in the clinical record.

When you approve an AI-generated note, you are making a professional statement. You are saying: I have reviewed this documentation and I am satisfied that it is an accurate record of the clinical encounter.

That is the same standard as any clinical note. It does not matter that the first draft was written by AI rather than by you. The moment you sign it, it is yours.

Would you sign a letter that a registrar wrote without reading it? Would you approve a discharge summary without checking the medication list? Of course not. Apply the same standard to AI-generated notes.

In the next lesson, we are going to look at what happens when the review process catches something wrong — the specific types of errors AI makes in clinical documentation, with real examples and practical guidance on how to correct them.