What If You Let ChatGPT Do All Your SOAP Notes for a Week?

SOAP notes. The cornerstone of clinical documentation—and the bane of a doctor’s already-packed day. Imagine this: you finish seeing a full roster of patients, and instead of sitting down for two hours of typing, you say, “Hey ChatGPT, can you take care of this?”

It sounds futuristic. Maybe a little reckless. Maybe even revolutionary. But what really happens when an AI language model drafts all your SOAP notes for a week?

Welcome to the experiment no one openly admits to trying—but almost every clinician has at least thought about.

Why Would Anyone Consider This in the First Place?

Let’s be brutally honest. Most doctors didn’t choose medicine because they enjoy writing. Documentation is a necessity, not a passion. The burden has escalated sharply in recent years, and the data tells a sobering story:

• Burnout is tightly linked to electronic medical record (EMR) usage and after-hours documentation.
  
  
• On average, clinicians spend two hours on administrative work for every single hour of face-to-face patient care.
  
  
• SOAP notes often feel more tailored for billing audits than for actual clinical value.
  

So when ChatGPT enters the picture promising to trim time, lower stress, and maybe even produce smoother, more organized prose—it becomes hard to ignore.

Setting the Ground Rules: What This "Experiment" Looks Like

To make this hypothetical realistic, let’s define the framework:

• The clinician either dictates or types in core clinical data: HPI, ROS, and exam findings.
  
  
• ChatGPT is then asked to generate the full SOAP format: Subjective, Objective, Assessment, Plan.
  
  
• The doctor carefully reviews and adjusts before pasting the content into the EMR.
  
  
• All of this takes place in a mock setting with non-identifiable, hypothetical data, to avoid any breach of current regulatory standards.
  

This isn’t about replacing doctors with a machine. It’s about using AI as a documentation co-pilot.

Day 1: Hesitant Optimism and the First “Wow” Moment

Skepticism is high at the start. But curiosity wins. A doctor enters this prompt into ChatGPT:

“45-year-old female with type 2 diabetes, presents with foot pain and numbness, worsening over the past month. No recent trauma. Physical exam shows decreased sensation to monofilament, intact pulses. HbA1c last month was 8.6%. Plan includes foot care education, referral to podiatry, consider starting pregabalin.”

ChatGPT replies:

S:
Patient is a 45-year-old female with type 2 diabetes presenting with a one-month history of progressive foot pain and numbness. She denies trauma or infection. Describes burning discomfort, especially at night, with increasing difficulty ambulating.

O:
Vitals: Stable
Physical Exam: Decreased monofilament sensation bilaterally. Dorsalis pedis and posterior tibial pulses are intact. No skin ulcers or breakdown noted.
Labs: HbA1c 8.6% (from prior month)

A:
Diabetic peripheral neuropathy; uncontrolled type 2 diabetes

P:
Start pregabalin 50 mg at bedtime, titrate gradually
Refer to podiatry for specialized foot evaluation
Reinforce diabetic foot care education and daily inspection routines
Increase frequency of blood glucose monitoring
Plan for HbA1c reassessment in 3 months

The physician pauses. “Did it just write that in 15 seconds? And it’s… good?”
Yes. Yes, it did.

Day 2–3: Speed Improves, But So Does Caution

The second and third days bring faster output. With minimal prompting, notes are produced in seconds. It’s efficient. It’s consistent. But soon, you start to see the edges.

• ChatGPT thrives on format—it produces sleek, uniform documentation.
  
  
• But it sometimes adds inferences or assumptions that weren’t explicitly mentioned.
  
  
• It tends to be verbose, especially in simple cases—useful for audits, but not always practical for fast-paced clinics.
  

Interestingly, doctors start optimizing how they feed information into the prompt. More clarity in input equals better clarity in output. This reciprocal sharpening of language reveals something deeper: working with AI improves your own documentation mindset.

Day 4–5: Edge Cases, Specialty Challenges, and Nuance

Here’s where it gets tricky. What happens when the cases become more specialized, less textbook?

Turns out, ChatGPT performs well in many scenarios—especially common internal medicine cases like hypertension, COPD, and diabetes management.

But when faced with more nuanced fields, things get complicated:

• Mental health notes are neatly structured, but the subtleties of emotional tone, psychiatric insight, and judgment calls are often too delicate for an algorithm.
  
  
• Surgical notes—particularly perioperative planning—tend to lack the depth of understanding unless explicitly instructed.
  
  
• Emergency cases might yield overly generic assessments unless the AI is given highly specific clinical details.
  

And it won’t cite up-to-date guidelines on its own unless asked—e.g., “write plan according to the ADA standards.”

The takeaway? ChatGPT isn’t careless. It’s just literal. It can’t read between the lines unless you put those lines there.

Day 6–7: The Honeymoon Phase Ends… or Evolves?

By the end of the week, the experience has matured. There are patterns:

• You’ve saved several hours—possibly 4–6—of typing and formatting.
  
  
• The notes you produce with AI assistance are neater, more coherent, and strangely, a little more human-sounding.
  
  
• Clinical sessions are more relaxed because the mental drain of late-night SOAP writing has eased.
  

But you’ve also learned the limits:

• This is not autopilot. You are still the physician, the final word, the interpreter.
  
  
• AI cannot replace your judgment, your experience, or your ethical responsibility.
  
  
• For emotionally charged or high-stakes conversations—palliative care, informed consent, DNAR discussions—human-authored language still matters more than anything else.
  

And yet, something is undeniably different now. The idea of documentation as a collaborative process—with AI as your assistant—feels closer than ever.

Legal, Ethical, and Practical Considerations

Let’s step out of the fantasy for a moment. The use of ChatGPT or any generative AI in clinical documentation is not yet approved for most real-world settings—unless wrapped within secure, HIPAA-compliant systems.

Important issues to consider:

• Data security: Never input actual patient information into public AI platforms.
  
  
• Legal ownership: The note, even if AI-assisted, is yours. You are accountable for its accuracy.
  
  
• Bias risk: AI can "hallucinate"—inventing plausible-sounding but inaccurate information.
  
  
• Audit challenges: Medical-legal scrutiny may question authorship if the content was AI-generated.
  

Until AI tools are fully embedded into EMRs with transparency and control, using them in real cases carries significant risk.

Is This Cheating? Or Smart Medicine?

Here’s a philosophical take.

Is using a stethoscope cheating over manual auscultation with the ear?
Is dictating notes instead of hand-writing them a moral shortcut?

We’ve always embraced technology that amplifies clinical efficiency and quality. ChatGPT is just the latest tool in a long evolution.

The difference? It requires judgment and ethical framing.
Using AI isn’t cheating. Misusing it—blindly trusting without verifying—might be.

But if you're using it to sharpen your output, clarify your thinking, and free up time to actually be present with patients, then it's a step forward.

What This Means for the Future of Medical Documentation

The writing is on the wall—and soon, possibly in your EMR too. ChatGPT-style tools may soon:

• Be integrated within EMRs like Epic, Cerner, and Meditech
  
  
• Sync with voice dictation systems to transcribe and organize SOAP notes on the fly
  
  
• Assist in real-time during patient visits, with clinician oversight
  

With adoption, expect to see:

• A reduction in meaningless documentation fluff
  
  
• Better alignment with actual clinical impressions and plans
  
  
• More human interaction, less keyboard time
  

But for this to work, we need:

• Institutional support and legal guardrails
  
  
• Clear documentation standards for AI-assisted notes
  
  
• Training for clinicians on effective AI prompting
  

This is not a plug-and-play moment—it’s a transition that needs education, regulation, and collective clinical feedback.

Final Verdict: Should You Try It?

If you’re intrigued, start in a safe sandbox. Create sample cases. Test prompts. Try rewriting your own notes through AI to compare clarity and structure.

You might find that it teaches you something—about how you write, what you emphasize, and where inefficiencies live in your process.

In the end, AI won’t replace doctors. But it might help unburden them.

That, in itself, is a prescription worth considering.