Are Your AI Notes Helping Or Hurting The Continuity Of Care?

You finish a patient visit, and a detailed AI clinical note awaits you. The efficiency is undeniable. But this efficiency is meaningless if that note confuses the next clinician or, worse, introduces errors into the patient's record.

The true value of AI clinical notes isn't just speed; it's their impact on continuity of care. A poorly implemented tool can create poor notes that confuse, rather than coordinate, thus fragmenting the patient's journey across providers.

Explore how to ensure that your notes actively support, not sabotage, patient treatment goals and seamless care coordination.

4 Ways AI Clinical Notes Can Hurt Continuity Of Care

While AI scribes are powerful tools, treating their initial output as a final product without clinical oversight introduces significant risks. Understanding these potential risks of AI notes is the first step toward mitigating them.

The Genericization Of Clinical Nuance

• The Problem: AI models are trained on vast datasets of clinical text, which can lead them to default to common, non-specific phrases that make your notes sound the same. This often strips away the unique, patient-specific details that form the bedrock of clinical reasoning.
• The Impact: When a note states, “patient presents for follow-up of diabetes and hypertension,” it checks a box but conveys nothing. The clinician cannot discern if the visit was routine or if it was triggered by hypoglycemic episodes. The narrative is lost.
• Additional Examples:
  • A Poorly Generated Note: “Patient counseled on diet and exercise”
  • A Note That Conveys Clinical Meaning: “Focused counselling provided on implementing a low-carbohydrate breakfast and lunch to address AM and pre-lunch hyperglycemia, per CGM data. Patient agreed to try swapping oatmeal for eggs for one week and will review glucose trends and the next visit.”

Perpetuating and Amplifying Inaccuracies

• The Problem: AI operates on the “garbage in, garbage out” principle. If the ambient speech-to-text engine mishears a word or the AI misinterprets context, it can propagate that error throughout the note. A misheard dosage (5mg vs 50 mg), a misaligned laterality (left vs right), or an incorrect allergy can become a serious issue.
• The Impact: This creates a direct patient safety risk. An error in the Assessment and Plan is bad enough, but when that same error is mirrored in the HPI, Review of Systems, and Physical Exam, it becomes a coherent, false clinical picture that can mislead the entire care team.

Creating Information Overload

• The Problem: AI’s capability to generate large volumes of text is a feature that can become a bug. In an attempt to be thorough, it may include every minor detail from the conversation, producing a bloated, unstructured “wall of text”. Critical findings, like a new red flag symptom or a subtle exam finding, get buried in a sea of irrelevant information.
• The Impact: This dramatically increases the cognitive load for other clinicians. A specialist reviewing the note for a specific consult question may miss a crucial piece of information.

Lacking a Clear Clinical Trajectory

• The Problem: The most valuable part of a clinical note is often the synthesis of information to create a forward-moving plan. Some AI scribes excel at documenting the past (HPI) and present (Exam), but fail to create a logical bridge to the future. The Assessment may be a simple list of diagnoses without connecting them to the data, and the Plan may contain generic actions that dont logically follow.
• The Impact: This disrupts the handoff. The next provider is forced to reconstruct the clinical reasoning themselves.

How AI Clinical Notes Help Continuity of Care

If the pitfalls in part 1 represent the risks of AI notes, what does success look like? A high‑quality AI clinical note should function as an extension of your clinical reasoning. Here are the key characteristics to look for:

• Prioritize Accuracy: The note must be a precise and trustworthy reflection of the encounter. This goes beyond just correct spelling; it means that medications, dosages, allergies, critical history, and exam findings are documented accurately and without error. It is a source of truth that other providers can rely on without double-checking.
• Ensure Clarity: The note is easy to scan and digest. It uses headings, bullet points, and bold text strategically to make critical information immediately apparent. It respects the reader's time by eliminating fluff and redundancy.
• Maintain a Logical Clinical Narrative: An excellent note tells a coherent story. The History of Present Illness has a logical timeline and progression. The Assessment (A) doesn't just list problems; it synthesizes the data from the Subjective (S) and Objective (O) sections to justify the diagnoses. This narrative demonstrates the “why” behind your decisions.
• Drive Action With a Forward-Looking Plan: The Plan (P) is the most critical section for continuity. It must be specific, assigning clear tasks and ownership. It answers the questions:
  • What needs to happen next?
  • Who is responsible?
  • By when?

This transforms the note from a historical record to a proactive tool for continuity of care with AI.

A Step-By-Step Guide To Evaluating Your AI Clinical Notes

Knowing the theory is one thing; applying it is another. Use this checklist to critically evaluate your next AI clinical note. Treat this not as extra work, but as an essential quality control process.

Step 1: Scrutinize for Specificity

AI sometimes defaults to vague, general statements. Your role is to ensure specificity.

• What to Do: Read the Subjective and Assessment sections and ask “so what?” for each statement. Replace any generic phrases (e.g, “counseled on lifestyle changes”) with precise details that explain the clinical rationale and the specific advice given.

Step 2: Verify Data Consistency

This is a critical safety check to prevent errors from propagating through the EHR.

• What to Do: Conduct a focused spot check on high-risk data points. Cross-reference the AI-generated medication list, allergies, and key physical exam findings against what you verbally confirmed with the patient. Pay close attention to dosages, laterality, and the presence of any “hallucinations” that were never discussed.

Step 3: Assess Structure and Scannability

A note that cant be quickly understood fails as a communication tool.

• What to Do: Give yourself a few moments to find the most critical new diagnosis, a changed medication, and the most urgent follow-up action. If you cant, the structure needs improvement. Ensure the note uses headings, bullet points, and bold text effectively to create a logical hierarchy of information, moving from a dense narrative in the HPI to a clear, scannable plan.

Step 4: Evaluate the “Handoff Readiness”

This is the ultimate test of the notes' value for care coordination.

• What to Do: Read the note from the perspective of a covering clinician or consultant. Ask: “What is my role here?” and “What is the explicit clinical question or task for me?” The Assessment must synthesize the data to justify the diagnoses, and the Plan must be action-oriented, assigning clear tasks and ownership (e.g., “nurse to schedule,” “patient to complete”), leaving no ambiguity about the next steps. By systematically applying this audit, you shift from being a passive proofreader to an active director of the AI’s output, ensuring every note you sign is a robust tool for high-quality care.

Conclusion

The ultimate measure of continuity of care with AI is not its speed or its length, but its contribution to a seamless, safe, and effective patient journey. An unaudited note risks becoming a source of error, while a carefully curated one becomes a powerful tool for care coordination.

By taking an active role as the clinical expert and applying the audit framework outlined here, you shift the relationship with your technology and move from being a proofreader to the director. This ensures that the efficiency gained translates directly into higher‑quality and continuous care.