Best ChatGPT Prompts for Doctors (2026)

One of the most time-consuming documentation tasks in outpatient medicine is producing plain-language patient education materials that match a specific patient's literacy level, language, and clinical situation. ChatGPT can produce a solid first draft in under 30 seconds given the right prompt.

**Example prompt:** > You are a physician writing a patient education handout. Write a one-page handout explaining [TYPE 2 DIABETES MANAGEMENT] for a patient with a [6th-grade] reading level. Include: (1) a one-sentence explanation of the condition, (2) the three most important lifestyle changes the patient should make, (3) how their medication works in plain language, (4) three warning signs that require a call or visit to the office, and (5) one resource they can look up for more information. Format with short paragraphs and bullet points. Do not use medical jargon. End with a reminder to ask their doctor any questions.

To adapt this prompt: replace the bracketed fields with the relevant condition and the literacy level appropriate for your patient panel. If you serve non-English-speaking patients, add "Translate the handout into [Spanish/Mandarin/etc.] after writing it." Always review the output against current clinical guidelines before printing or sharing — the model may omit important information or reflect slightly outdated recommendations.

For more structured guidance on using AI roles in clinical communication, see our role prompts for doctors guide.

After-visit summaries (AVS) are required under many patient access standards and are a significant documentation burden per visit. ChatGPT can convert a brief narrative dictation into a formatted AVS — provided you never input PHI into consumer ChatGPT.

**Example prompt:** > You are a medical scribe creating an after-visit summary for a physician. Based on the following visit notes, write a structured after-visit summary. Use plain language the patient can understand. Include: (1) reason for today's visit, (2) key findings or test results discussed (in plain language), (3) medications — any new prescriptions, changes, or renewals — with one-sentence explanations of what each is for, (4) follow-up instructions including who to call and when, (5) any referrals ordered. Format as a patient-friendly document with headers. > Visit notes: [Paste your de-identified visit narrative here]

The critical step is using a de-identified narrative. Describe the encounter using age, sex, and clinical details only — no name, date of birth, MRN, address, or any other identifier. Example: "56-year-old male presenting with three weeks of exertional dyspnea. No PHI included." Once the draft is generated, you add identifying information in your EHR environment, not in ChatGPT.

For nurses performing similar documentation tasks, see our parallel guide on role prompts for nurses.

ChatGPT is not a diagnostic system and must never be used as one. However, it can function as a rapid brainstorming tool — a way to surface diagnoses you might want to consider or rule out, similar to talking through a case with a colleague. The value is in the conversation, not the output as a final answer.

**Example prompt:** > You are helping a physician think through a clinical case as a brainstorming aid — not as a diagnostic system. This is a thinking exercise, not a replacement for clinical judgment. Here is a de-identified case summary: > [45-year-old woman with a 2-week history of progressive fatigue, diffuse joint pain, photosensitivity, and a malar rash. No significant past medical history. No current medications.] > List the top 8-10 diagnoses on the differential for this presentation, grouped by likelihood (most likely first). For each diagnosis, note one or two key clinical features or tests that would help confirm or rule it out. Format as a table.

After reviewing the output, you might follow up with: "Which of these would be most important to rule out urgently and why?" or "What are the cheapest initial tests to narrow this differential?" Treat each answer as a prompt for your own reasoning, not a conclusion. For a broader discussion of how AI fits into clinical workflows, see our overview of AI for healthcare.

Keeping up with the literature is one of the hardest parts of practice. ChatGPT can compress a dense abstract or methods section into a three-paragraph clinical summary — useful for rapid appraisal or sharing findings with colleagues.

**Example prompt:** > You are a clinical researcher summarizing a study for a busy physician. Here is the abstract of a published paper. Summarize it in plain language using this structure: (1) What question did the study try to answer? (2) How was it designed — study type, population, sample size, duration? (3) What were the key results, including primary and secondary endpoints? (4) What are the most important limitations or caveats a clinician should know? (5) What is the one-sentence clinical take-away, if any? Be honest about uncertainty — if the study has major limitations, say so clearly. > [Paste abstract or article excerpt here]

One important caveat: if you paste an abstract, verify it is the actual abstract from the published paper. The model cannot access PubMed or full-text journals in real time, and if you paste a paraphrased or misquoted version, the summary will reflect that. For deep-dive systematic reviews and research tools, see our guide to the best AI for medical research in 2026.

Medication counseling is time-constrained in most outpatient visits. A well-crafted ChatGPT prompt can produce a plain-language medication explanation that you review and hand to the patient as a printed card or paste into an after-visit message.

**Example prompt:** > Write a plain-language explanation of [METFORMIN] for a patient who has just been prescribed it for [TYPE 2 DIABETES]. Write at an 8th-grade reading level. Cover: (1) what the medication does in simple terms, (2) how to take it and when, (3) the three most common side effects to watch for and what to do about them, (4) one important interaction or contraindication to be aware of, (5) what to do if they miss a dose. Use short sentences and bullet points. End with: 'Ask your pharmacist or doctor if you have any questions.'

The same template works for procedure explanations: replace the drug fields with the procedure name and adjust the sections accordingly (e.g., what to expect the day of, recovery instructions, when to call the office). Always verify drug information against the current prescribing information or a trusted clinical reference such as your institution's pharmacy database before sharing with patients.

This approach also applies in dental contexts — our guide on best ChatGPT prompts for dentists in 2026 covers the same framework adapted for dental procedures and patient communication.

A referral letter that clearly communicates the clinical question, relevant history, and urgency level is one of the most impactful pieces of clinical communication you send. ChatGPT can format a clinically coherent referral from a brief narrative — saving 5-10 minutes per referral.

**Example prompt:** > You are a physician drafting a formal referral letter to a specialist. Based on the clinical summary below, write a professional referral letter. Include: (1) reason for referral — the specific clinical question you want the specialist to address, (2) relevant clinical history — pertinent positives and negatives only, (3) current medications (list format), (4) relevant results (labs, imaging) — summarize findings, do not reproduce raw data, (5) urgency — routine / semi-urgent / urgent, (6) what you have already tried or ruled out, (7) your contact information placeholder [REFERRING PHYSICIAN NAME, PHONE, FAX]. Keep the letter to one page. Use formal medical letter format. > Clinical summary (de-identified): [Paste narrative here]

After generating the draft, paste it into your EHR and add the patient identifiers, dates, and your signature block there — not in ChatGPT. The model reliably handles letter structure and transitions; the physician provides clinical accuracy.

For a look at how AI tools are being used across broader clinical documentation, including chart review, see our guide to AI for medical chart review.

Prior authorization (PA) requests are among the most administratively burdensome tasks in US medicine. A well-structured PA letter — one that leads with the ICD-10 codes, references step-therapy completion, and links the clinical rationale to the payer's criteria — is significantly more likely to be approved on first submission.

**Example prompt:** > You are a physician writing a prior authorization letter to an insurance company. Write a formal, structured prior authorization request for the following. Use this format: (1) Patient demographics placeholder [AGE, SEX, INSURANCE ID], (2) Requested medication or procedure: [ADALIMUMAB — BIOSIMILAR], (3) ICD-10 diagnosis codes: [M05.79, Z87.39], (4) Clinical rationale: explain in 2-3 sentences why this treatment is medically necessary for this patient's condition, (5) Step therapy: confirm the following treatments were tried and failed — [METHOTREXATE 20mg for 12 weeks — inadequate response; HYDROXYCHLOROQUINE 400mg for 6 months — inadequate response], (6) Supporting evidence: one or two sentences referencing current clinical guidelines supporting the use of this agent for this indication, (7) Urgency and clinical consequences if denied. Close with the physician's contact information placeholder. Keep the letter under 400 words.

One practical tip: many payers post their specific PA criteria online. If you can find and paste the payer's criteria into the prompt, you can instruct the model to structure the letter to address each criterion point by point — which dramatically improves first-pass approval rates. Adjust the step-therapy and ICD fields for each case.

SOAP note documentation is the most common physician writing task and one of the most automatable. ChatGPT can take a free-form spoken narrative and impose SOAP structure — making it far faster to review and finalize than writing from scratch.

**Example prompt:** > You are a medical scribe. Convert the following dictated encounter narrative into a properly formatted SOAP note. Use standard SOAP structure: Subjective, Objective, Assessment, Plan. Under Assessment, include the primary diagnosis and relevant differential diagnoses as numbered items. Under Plan, separate medications, orders, patient instructions, and follow-up into sub-sections. Keep the language clinical and precise. Do not add clinical information that is not in the narrative. If information is missing for a section (e.g., vitals not mentioned), write '[Not documented]' as a placeholder. > Encounter narrative (de-identified): [Paste de-identified narrative here]

The 'do not add clinical information' instruction is critical — without it, the model may fill in plausible-sounding but fabricated details to complete the note structure. Always review generated SOAP notes against your actual recall of the encounter. Any discrepancy should be corrected before signing.

If your organization is evaluating AI scribing tools built on language models, the HIPAA compliance layer matters enormously. Tools like Ambient AI scribes typically operate under a BAA. For a full picture, see HIPAA and AI: 2026 state of compliance.

ChatGPT can suggest ICD-10 and CPT codes based on an encounter description and explain the documentation requirements for each code level. This is most useful as a quick sanity check or a tool for physicians who handle their own coding in smaller practices.

**Example prompt:** > You are a medical coding assistant helping a physician review an outpatient encounter for appropriate ICD-10 and CPT codes. Based on the de-identified encounter summary below, suggest: (1) the primary ICD-10 diagnosis code and its description, (2) any relevant secondary or comorbidity codes, (3) the most likely CPT code for the evaluation and management (E/M) service based on the documented complexity, with a one-sentence explanation of why that level is appropriate, (4) any additional CPT codes for procedures or services performed. For each code, note the documentation element that supports it. Flag any areas where the documentation may be insufficient to support the code level. > Encounter summary (de-identified): [Paste here]

Coding suggestions from ChatGPT are not authoritative and must be reviewed by the treating physician or a certified medical coder before submission. The model does not have access to your EHR documentation, the full encounter record, or current payer-specific rules. Use these outputs as a starting point and a learning tool, not a final answer.

Reading and absorbing CME articles is a core professional obligation that competes with a full clinical schedule. ChatGPT can produce a structured digest of a full-text article that captures the study design, key results, clinical implications, and limitations in a format you can read in two minutes.

**Example prompt:** > You are a clinical educator summarizing a medical journal article for a physician seeking continuing education credit. Here is the full text of the article. Produce a structured digest with the following sections: (1) Study question and clinical context — why does this matter to a practicing physician? (2) Study design — type (RCT, cohort, meta-analysis, etc.), population, n, follow-up duration, key inclusion and exclusion criteria, (3) Primary outcomes — what was measured and what did the study find? Include absolute risk differences where reported, not just relative risk, (4) Secondary outcomes and subgroup analyses — any notable findings, (5) Limitations — list the most clinically relevant ones, (6) Generalizability — which patient populations does this apply to, and which does it not? (7) Clinical bottom line — one or two sentences on what this means for your practice, if anything. If the evidence is weak or preliminary, say so explicitly. > [Paste article text here]

A few important notes on this use case: absolute risk differences matter more than relative risk for clinical decision-making, so specifying that in the prompt helps. The instruction to flag weak or preliminary evidence is important — without it, the model tends to present all findings with equal confidence. For a broader look at how AI tools are reshaping medical research workflows, see best AI for medical research in 2026.

Every prompt in this guide is designed to be used with de-identified, placeholder-only input in consumer ChatGPT. This is not a best practice — it is a legal and ethical requirement. Under HIPAA, protected health information (PHI) includes not only names and dates of birth, but also dates of service, geographic identifiers, and any other information that could be used to identify a patient. Consumer ChatGPT does not have a BAA with healthcare entities and is not an appropriate environment for PHI of any kind.

If your organization wants to use AI in a HIPAA-compliant way, the options include: (1) OpenAI's enterprise tier, which offers BAA coverage for eligible customers — see OpenAI's usage policies for current details; (2) Microsoft Azure OpenAI Service with a BAA via Microsoft's healthcare agreements; (3) purpose-built clinical AI tools (ambient scribes, EHR-embedded AI) that are designed and contracted to handle PHI. For a full breakdown of the current compliance landscape, our HIPAA and AI: 2026 state of compliance guide covers the major platforms and their BAA status.

Beyond HIPAA, the clinical safety guardrails are straightforward: ChatGPT is a language model. It predicts text. It is not trained or validated as a clinical decision support tool, it does not have access to current clinical guidelines or literature in real time, and it can produce plausible-sounding but factually incorrect clinical content. The prompts in this guide are scoped to drafting and administrative tasks for exactly this reason. When in doubt, verify every clinical statement in any ChatGPT output against a trusted clinical reference before acting on it or sharing it.

The ten examples in this guide are starting points. Building a personal or practice-wide prompt library that reflects your specialty, patient population, and documentation style will multiply the value significantly. A few principles that make physician prompts more reliable:

First, always assign a role at the start of the prompt ("You are a physician writing...", "You are a medical scribe..."). This reliably shifts the model's default tone and vocabulary toward clinical register. Second, specify the output format explicitly — "format as a table", "use SOAP structure", "bullet points under three headers" — rather than letting the model choose. Third, use placeholders in brackets [LIKE THIS] for every field that will change between uses. This lets you build a template you can reuse by replacing only the bracketed sections.

Fourth, include a negative instruction for the most common failure mode. For SOAP notes that is "do not add clinical information not in the narrative." For differentials it is "do not suggest a diagnosis — this is a brainstorming aid." For literature summaries it is "if the evidence is weak or preliminary, say so." These instructions keep the model's tendency to fill in plausible detail from causing problems in clinical contexts. For a cost-efficient way to run these prompts at volume if you are building a practice workflow or tool, see our AI Prompt Cost Calculator.