Run AI prompts for doctors
on every patient conversation.

A different approach to AI prompts for doctors.

An AI prompt for doctors is an instruction, run over a patient conversation, dictation, or care-team meeting, that turns spoken clinical detail into something a chart, a referral, or a colleague can use. Physicians have always done this work by hand: replaying a visit in their head at 9pm to write it up, retyping the same history into a referral letter, guessing at whether a diagnosis actually landed with a nervous patient. Prompts written for generic office tasks were never built for that job.

Why generic prompts break down in a clinic

Most AI prompt libraries assume you are typing a question into a chatbot with nothing behind it. A doctor's day is not typed, it is spoken: a consult, a dictation, a hallway consult with a specialist, a case conference. Copy-pasted prompts from a general list have no transcript to run against and no memory of what was actually said in the room, so the doctor still has to write the summary from scratch afterward, usually after hours, usually from memory.

Reading the visit, not just the transcript

Speak AI treats a consult recording the way a careful colleague would, at machine speed. Each conversation is transcribed in your language, with 100+ supported, and then the recording itself is read on three layers: the words spoken, the tone and energy behind them, and, where relevant, what was shown or shared on screen during a telehealth visit. That includes how something was said, not just what: whether an explanation landed calmly or rushed past a worried patient, the same read a coaching manager gets from call scoring en coaching applied to sales and service conversations. Trends surface across weeks of visits instead of one file at a time, and dashboards die u kunt aanpassen en white-label track documentation time, tone, and follow-up completion over time.

Prompts doctors run on their conversations

• Visit recap: "Summarize this consult into chief complaint, history, exam findings, assessment, and plan, ready to paste into the chart."
• Differential follow-up: "List every symptom this patient mentioned, and flag which ones I have not asked a follow-up question about yet."
• Referral letter: "Draft a referral letter to cardiology using the history and findings discussed in this visit, in a clinical tone."
• Patient-communication tone review: "Rate how clearly I explained the diagnosis and next steps, and flag any moment my tone may have read as rushed."
• Medication mentions: "List every medication and dosage the patient mentioned during this visit, flagged for pharmacist review."
• Case conference synthesis: "Condense this multidisciplinary case discussion into the agreed plan and each specialist's follow-up action."
• Journal club recap: "Summarize the key findings and clinical implications discussed in this journal club recording."

From dictation to a ready chart note

The result is documentation that keeps pace with the visit instead of trailing it by a week. A healthcare marketing and consulting firm put its client session recordings through this workflow and saved $190K and over 10,000 hours, cutting session processing from eight hours to roughly eighteen minutes. And because a chart note is only useful where the team already works, the same structured data is queryable straight from Claude, ChatGPT, and Cursor through the MCP server, alongside the API and integrations layer. Enterprise builds support BAAs for teams that need them, though no software vendor can promise blanket HIPAA compliance on its own: the safeguards depend on how your practice configures access, storage, and retention around the platform.