Is Your AI Writing Notes You’d Actually Submit? Here is the Test

If you've ever reviewed an AI-generated SOAP note and thought, “I’d never write it that way”, you're not alone. Many AI scribes produce SOAP notes that feel generic, miss critical nuance, or contain subtle errors that could impact care or compliance. But how can you quickly tell whether your AI tool is truly helping, or creating more work?

Put your AI to the test with this clinical checklist designed to catch the most common and consequential mistakes. Here's what to look for before you sign.

What Makes an AI-Written SOAP Note ‘Submission-Ready’?

A submission‑ready note is more than just accurate text; it's a clinically coherent, compliant, and efficient document that reflects your judgment. Here's what that looks like in practice.

• Clinical Accuracy & Completeness: All data is factually correct. The objective section includes actual vitals and exam findings, the assessment is supported by data, and the plan has clear, actionable steps.
• Logical Consistency: The note tells a logical story. The assessment and plan are perfectly aligned; a diagnosis is followed by a treatment.
• Patient Voice Preserved: The subjective section captures the patient's own words for crucial diagnostic context rather than sterile summaries. For a deeper dive on this, read our guide on how to make SOAP notes sound more human.
• Proper Structure and Flow: Information is placed in the correct SOAP sections. Vitals are in Objective, not Subjective; the treatment plan is in Plan, not Assessment.
• Audit-Ready Compliance: The note justifies the level of service billed and meets all regulatory requirements (e.g., AMA E/M Documentation Guidelines) to prevent denials or audit flags.

The “Good Enough” Aspect for Clinicians

For most clinicians, ‘good enough” isn't perfection; it's a solid draft that requires minimal, high‑value edits to sign. It means the AI has done the heavy lifting, and you're applying your clinical expertise to refine, not rewrite.

• The Foundation is Correct: The raw data (vital, medications, history) is accurately captured and in the right place. You're fixing nuance, not fact.
• The Clinical Story is Mostly There: the connection between S, O, A, and P is logical even if it requires slight tightening. You're confirming the logic, not building it from scratch.
• No Critical Errors: There are no hallucinations, misdiagnoses, or dangerous omissions. Your edits are for precision and style, not patient safety.
• Efficiency is achieved: The time spent reviewing and editing the AI-generated draft is significantly less than the time it would have taken to write the note entirely yourself.

Key Aspects of Assessing Submit-Ready AI-Written SOAP Notes

How to Evaluate AI-Written SOAP Notes: Common Assessment Frameworks & Pitfalls

Ensure AI‑generated notes meet clinical standards using structured evaluation methods. Key frameworks include:

Physician Documentation Quality Instrument (PDQI):

Validated tool measuring:

• Thoroughness: Completeness of history, exam, and assessment.
• Accuracy: Factual correctness of data and timelines.
• Usability: Effectiveness for clinical communication and decision-making.

Customized AI Evaluation Frameworks

Often Include:

• Clinical Logic Alignment: Rigor of links between assessment and plan.
• Contextual Fidelity: Preservation of patient information in subjective sections.
• Data Integrity: Correct placement/sourcing of structured data (e.g, vitals)

Critical AI-Specific Pitfalls

• Omission Errors: Missing critical details (e.g, failing to document radiating pain in a chest pain case)
• Addition Errors: Including unsupported information (e.g., incorrect medical history or med allergies)
• Hallucinations/Fabrication: Generating entirely fabricated findings (e.g., documenting an exam that never happened). This is a known issue with LLMs.
• Misinterpretation of Audio: Transcribing spoken words incorrectly (e.g., “no thyroid disease” - “history of thyroid disease”)

Best Practices for Ensuring Quality in AI-Written SOAP Notes

Implementing these core practices ensures AI‑generated medical documentation is safe, accurate, and clinically valid.

Enforce Clinician Oversight

• Mandate human-in-the-loop review for every note, treating AI output as a draft requiring verification and signature.
• Empower providers to add nuance and correct inaccuracies, ensuring the final note reflects their clinical judgement.

Commit to Ongoing Monitoring

• Conduct regular quality audits using structured frameworks to track accuracy and completeness over time.
• Establish formal feedback loops where clinicians' corrections are used to retrain and improve the AI system.

Conduct Robust Testing with Real Scenarios

• Pilot the AI across diverse specialties and acuity levels before fully launching.
• Stress test the system with edge cases, such as complex histories.

Implement Ethical & Safety Safeguards

• Choose transparent tools and embed clinical guardrails (e.g., flagging diagnoses without supporting data)
• Proactively audit for and mitigate biases to ensure equitable medical documentation across patient demographics.

How to Test Your AI Scribe and Know Your SOAP Notes Are Good Enough to Submit

Before fully committing to an AI scribe in your workflow, put it through a rigorous, real‑world trial. Start by running it through a series of diverse patient encounters and meticulously compare its output against your own mental model of a perfect note.

A note is truly “good enough to submit” not when it's flawless, but when the edits required are minimal and high‑value, focusing on refining clinical nuance rather than correcting factual errors or hallucinations.

The ultimate test is a clear return on time: the review and sign‑off process should be significantly faster than writing the note from scratch, without compromising your medical documentation standards or patient safety.

How Twofold Helps You Run the Test the Right Way

• Specialty-Specific Templates: Get effective soap notes with the right structure from the start, slashing editing time.
• HIPAA-Compliant Guardrails: automatically aligned assessments and plans prevent dangerous mismatches and protect patient information.
• Adaptive Learning: The AI learns from your corrections during the trial, constantly improving.
• If you want more information on solid SOAP notes, see our guide on how to write them with clinical accuracy or consult our template.

Conclusion

Choosing an AI Medical Scribe isn't about finding the perfect tool; it's about finding a reliable partner. The right solution should augment your expertise, not replace judgment. By focusing on clinical accuracy, seamless integration, and– most importantly: uncompromising clinician oversight, you can harness AI’s efficiency to reclaim time for patient care while safeguarding documentation integrity.

The goal is a sustainable partnership where technology handles the administrative burden, and you remain in full command of clinical decisions. With careful evaluation and the right platform, this balance is achievable.