Oct 5, 2025
6
min read
Medically Reviewed
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What We Mean by Clinical Note Quality
Before asking whether AI can improve note quality, it is worth defining what quality means in the context of clinical documentation. A high-quality clinical note is accurate, complete, structured and clinically useful. Accuracy means that the medical facts — diagnoses, medications, test results, clinical findings — are correctly recorded. Completeness means that all relevant information from the consultation is captured, including the patient’s presenting complaint, history, examination findings, assessment and plan. Structure means that the note follows a recognisable framework such as SOAP that enables another clinician to quickly locate the information they need.
Clinical usefulness is the ultimate measure. A note that is accurate and complete but buried in irrelevant detail is less useful than a shorter, well-structured note that highlights the key clinical decisions. Similarly, a note that follows SOAP format but contains significant omissions may satisfy an auditor but fail the clinician who needs to understand the patient’s trajectory at a glance. Quality is multidimensional, and any claim that AI improves note quality must be evaluated across all of these dimensions, not simply in terms of word count or typing speed.
The research literature has developed several validated instruments for assessing clinical note quality, including the QNOTE tool and the Physician Documentation Quality Instrument. These tools evaluate notes across domains such as comprehensiveness, organisation, and clinical relevance, providing a standardised way to compare AI-generated notes with manually produced records. Studies using these instruments have begun to accumulate evidence on how AI scribes perform relative to traditional documentation methods in real clinical settings.
The Evidence on Completeness and Accuracy
The most consistent finding in the emerging research is that AI-generated clinical notes are more complete than manually typed notes. A study published in the Journal of Medical Internet Research compared ambient AI scribe notes with traditional physician-dictated and typed notes across a sample of primary care consultations and found that the AI-produced notes captured significantly more clinical concepts per consultation. The difference was most pronounced for elements that are frequently omitted in manual notes: patient history details, social and family history, and the specific language patients used to describe their symptoms.
Accuracy is a more complex measure, because it depends on what is being compared. When measured against a gold-standard transcription of the consultation audio, AI-generated notes have been shown to have comparable or lower error rates for clinical facts than manually typed notes, particularly when the manual note was produced under time constraints. Where AI notes do show higher error rates is in the classification of clinical concepts — for example, correctly identifying whether a symptom belongs in the history or the physical examination — though this gap narrows with each generation of the technology.
Importantly, the errors in AI-generated notes tend to be different from the errors in manually produced notes. Human errors are predominantly errors of omission — details that were forgotten or deprioritised during typing. AI errors are more often errors of misattribution — a detail that is factually correct but placed in the wrong section of the note. For clinical decision-making, omission errors are generally considered more dangerous than misattribution errors, because missing information is more likely to lead to an incorrect conclusion than information that is present but slightly misplaced. This distinction matters when evaluating the overall safety profile of AI-assisted documentation.
Expert Tips
"Note quality has always been about the clinician's memory and typing speed rather than the clinical content of the consultation. That is a structural problem, not a personal failing. An AI scribe captures what actually happened in the room, not what the clinician could remember and type in sixty seconds after the patient left. When you separate documentation from recall, you immediately see that the ceiling we thought we were hitting was not the ceiling of good documentation at all — it was the ceiling of manual data entry during a busy clinical day." — Arash Zohuri, CEO, MediQo
SOAP Structure and Beyond
One of the most significant ways AI improves clinical note quality is through enforced structure. A manually typed note may follow SOAP format, or it may be a loosely organised paragraph, a list of bullet points, or a set of abbreviated phrases that the author can interpret but others cannot. The structure of a handwritten or typed note depends on the individual clinician’s habits, training and current fatigue level. An AI scribe, by contrast, can be configured to produce notes in a consistent format every time, with subjective, objective, assessment and plan sections clearly delineated and populated from the appropriate parts of the consultation.
The value of consistent structure should not be underestimated. A practice that implements AI scribing across multiple clinicians suddenly has clinical notes that look the same regardless of which GP wrote them. This is transformative for practice nurses who review care plans, for locums covering leave, and for the practice manager who needs to audit documentation quality across the team. It also makes it far easier to extract structured data from notes for clinical research, quality improvement and accreditation purposes. The RACGP’s Standards for General Practices require that clinical records be legible, dated and comprehensible, and a consistently structured AI note meets that standard more reliably than variable manual documentation.
MediQo Clinical Assistant produces structured notes that align with the documentation frameworks Australian GPs already use, including SOAP format, while also capturing the unstructured conversational detail that feeds clinical understanding. The clinician reviews and signs off on the note, so the final record combines the consistency of AI-generated structure with the clinical judgement of the human author. This hybrid approach preserves the strengths of both — the AI ensures nothing is missed and the structure is sound, while the clinician applies the interpretive expertise that no algorithm can replicate.
Key Takeaways
Studies demonstrate AI-generated clinical notes are more complete and contain fewer omissions than manually typed records.
Structured output from AI scribing follows SOAP and other frameworks, making notes easier to review and action.
Better note quality flows through to downstream benefits in billing accuracy, care planning and clinical audit.
Clinicians who use AI scribing report significantly less cognitive burden at the end of the day.
For as long as clinical notes have existed, their quality has been constrained by a simple fact: a human being has to produce them, usually under significant time pressure and while managing the cognitive demands of a live consultation. The result is a clinical record that varies enormously between clinicians, between practices, and even between consultations by the same GP on different days. Some notes are comprehensive and clinically useful; others are sparse, formulaic, or so heavily abbreviated that their meaning is opaque to anyone other than the original author. The healthcare system has largely accepted this variability as an inevitable feature of human work.
The question of whether artificial intelligence can improve clinical note quality is therefore a question about consistency and completeness. If an AI scribe can produce notes that are more structured, more detailed, and more clinically useful than the average manually typed record, then the potential benefit extends far beyond saving time. It means that the clinical record — the foundational document of all healthcare delivery — becomes a more reliable tool for clinical decision-making, care coordination, billing and audit. It means that the variability that has always been accepted as normal may no longer be necessary.
This article reviews the research on AI-generated clinical note quality, examining the evidence on completeness, accuracy and adherence to clinical documentation standards such as SOAP. It also explores how better structured notes from MediQo Clinical Assistant create downstream value in everything from MBS billing to chronic disease management, and what Australian GPs should look for when evaluating whether an AI scribe can genuinely improve the quality of their documentation.
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