10 SOAP Note Mistakes AI Still Makes (And How to Fix Them)

AI medical scribes leverage large language models (LLMs) to transform clinical conversations into structured SOAP notes, offering a powerful solution to the clinical documentation burden. However, these tools are not reasoning clinicians but statistical predictors of language, a distinction that introduces critical risks. Without clinical safety precautions, AI consistently makes errors that threaten patient safety and note integrity.

Understanding these failure modes is essential for safe implementation. Explore the 10 most common technical SOAP note errors and what strategies are needed to correct them efficiently.

10 Most Common AI SOAP Note Mistakes

1. Missing or Misplaced Vital Signs: Forgetting to include vital signs, or placing them in the wrong section, can lead to incomplete or confusing documentation.
2. Overgeneralizing in the Subjective Section: Summarizing patient statements too broadly may overlook important details about symptoms, concerns, or context.
3. Misaligned Assessment and Plan: When the plan doesn't clearly address the assessment, it creates gaps in clinical logic and continuity of care.
4. Inaccurate or Incomplete Objective Data: Skipping physical findings, test results, or observations undermines the accuracy of the clinical record.
5. Repeating Errors Across Sessions (Copy-Paste Pitfalls): AI might pull forward an outdated problem from a previous note, even if the current visit is for an unrelated issue.
6. Copy-Paste Artifacts From Prior Notes: Where AI copies text or instructions not meant for the patient's chart, such as including template placeholders like [insert exam findings here] into the final note.
7. Incorrect Section Placement (S/O/A/P Confusion): AI can misclassify critical information, placing a physical exam finding (Objective) in the Assessment section.
8. Ignoring Patient's Voice or Contextual Nuance: Sanitizing a patient's expressive language, stripping away crucial context.
9. Inconsistent Terminology Across Sessions: Describing the same condition differently in sequential notes, which can create confusion.
10. Missing or Vague Follow-Up Instructions: A generated plan may be non-actionable, suggesting "follow up as needed" instead of the specific, clinically appropriate instruction.

Why AI SOAP Note Mistakes Still Happen

The errors in AI‑generated SOAP notes are not random; they are systematic failures stemming from the fundamental way AI is built and integrated. Understanding these root causes is the first step toward mitigating them.

Limitations of Generic LLMs in Clinical Contexts

While large language models (LLMs) can generate fluent text, they have notable limitations in clinical settings that impact accuracy and reliability:

• Lack of Critical Reasoning: AI models are trained on large amounts of general text to predict the next most likely word. They lack a true understanding of human pathophysiology or the intent behind treatment plans.
• Hallucination Errors: to sound coherent, LLMs will often ‘hallucinate’ or fabricate plausible-sounding details to fill in gaps.

Insufficient EHR/Device Integration

When AI systems operate in isolation from clinical data systems, they miss critical contextual information that would inform accurate documentation.

• Data Silos: Without deep, two-way integration with the EHR, the AI operates on the audio alone. It cannot directly query the patient’s last known vitals or medications to validate information, which leads to inconsistencies.
• Lack of Automated Intake: Vitals from connected devices are not automatically integrated into the note. The AI must hear and transcribe them verbally, a process prone to error.

Lack of Structured Prompt Design or Guardrails

Without careful constraints and specialized prompting, AI systems generate documentation that lacks the structure and precision required in clinical settings.

• Unconstrained Generation: Without strict, specific templates and instructions, the output will be inconsistent.
• No Validation Rules: The system lacks built-in rules to flag potential errors, such as an assessment of ‘UTI’ with no corresponding objective data in the note.

Poor Understanding of Clinical Intent or Next Steps

AI systems often struggle to bridge the gap between documentation and clinical action, creating notes that don't drive patient care forward.

• Action vs Observation: AI can describe a current state but struggles to infer and articulate the necessary clinical actions.
• Disconnected Workflow: The AI generates a narrative note but fails to trigger the requisite clinical workflows, such as scheduling follow-ups or sending referrals, resulting in a disconnect between clinical documentation and proper care delivery.

No Human-in-the-Loop Review or Feedback

The absence of clinical oversight and adaptive learning mechanisms prevents AI systems from improving and ensuring accuracy over time.

• Unsupervised Automation: Treating AI output as the final product, rather than a draft, bypasses essential clinical oversight.
• Lack Of A Learning Loop: Many systems operate statically. If a clinician consistently has to correct terminologies, the AI does not learn from that feedback to improve its terminology for that specific user or practice, perpetuating the error.

How To Prevent AI SOAP Note Errors

Proactive design and integrated guardrails are essential for transforming raw AI output into a clinically accurate SOAP note.

Best Practices For AI-Generated SOAP Notes

1. Build Notes Around Standardized Clinical Language: Use structured, industry-standard terminologies like SNOMED CT to ensure consistency and reduce ambiguity in AI-assisted notes.
2. Tie Plan Elements to Diagnoses and Goals: Ensure every proposed intervention, medication, or test in the plan logically stems from a problem identified in the assessment.
3. Use Structured Prompts and System Instructions: Move beyond basic commands. Implement detailed, role-specific prompts that instruct the AI on the required data fields, format, and style to improve accuracy.
4. Avoid Auto-Fill Without Oversight: Never allow AI to finalize and sign a note automatically. Treat all AI output as a draft that requires mandatory review.
5. Validate With Clinician Feedback Loops: Implement a system that allows clinician corrections to be fed back into the AI model. This continuous feedback allows the system to learn from mistakes and adapt to a specific provider’s styles and preferences over time.
6. Preserve the Patient's Voice in Subjective Sections: Instruct the AI to directly quote the patient’s own words for key symptoms within the subjective section, avoiding over-generalization.

The Risks Of Ignoring AI Mistakes When Generating SOAP Notes

How Twofold Reduces SOAP Note Errors With AI

Twofold prevents common AI documentation errors by working directly with your EHR. It automatically pulls information like vitals and medications into the right sections of the note, so nothing is omitted or inaccurately placed. It also utilizes templates to ensure that diagnosis and treatment plans always match up.

Most importantly, Twofold doesn't replace your judgment; it supports it. The AI writes a first draft, but you always review and edit it before it's final. This ensures the note is accurate and sounds like you. With continuous feedback, Twofold's adaptive engine refines its accuracy and terminology over time to match your style.

Conclusion

AI‑generated SOAP notes present a powerful solution to administrative burden, but only if implemented with precision. The key to success lies in acknowledging their limitations: systematic errors in clinical reasoning, data placement, and nuance. Mitigating these risks requires more than just technology; it demands a disciplined strategy of structured templates, EHR integration, and mandatory clinical oversight.

By adopting these guardrails, practices can securely harness AI’s efficiency to enhance, not replace, clinical expertise, ensuring documentation is both accurate and patient‑centered. The goal is a seamless partnership that protects care quality and provider trust.