Voice to text for doctors: the three things it can mean The three things people mean by it, why the language setting matters for Indian clinics, and what Hinglish does to accuracy.

Voice to text for doctors is software that turns what a doctor says into written text, either as plain transcription, as extraction of specific items such as medicine names, or as generation of a whole note from the speech. Vendors sell all three under the same label, which is why one clinic's "voice EMR" is a dictation box and another's is an AI medical scribe. The difference decides how much the doctor has to read before anything is saved.

What is voice to text for doctors?

Transcription is the oldest form and the easiest to judge: read the text against what was said. Extraction adds a second model that reads the transcript and keeps a defined subset; the rest is discarded. Generation adds a model that composes. Each step is a place where an error can enter, so the longer the chain, the more review the output needs before it reaches the medical record.

Where does the definition come from?

There is no Indian statutory definition of medical dictation software, and no council rule treats a voice-typed prescription differently from a typed one. The vocabulary comes from the speech vendors. Microsoft's language support page for Azure AI Speech, read on 4 Nov 2026, describes its speech to text service in terms of "real-time transcription", "fast transcription" and "batch transcription", with a table of locales each one supports. In that table, the locale en-IN is "English (India)" and hi-IN is "Hindi (India)", and both are marked as supporting real-time transcription. A separate custom speech table shows both locales accept a "Phrase list", which is how a vendor can bias recognition towards a clinic's own drug names.

The locale is the setting that matters most for an Indian clinic and the one most demos skip. en-IN and hi-IN are different recognition models. en-IN expects English spoken with Indian accents and vocabulary; hi-IN expects Hindi and writes Devanagari. A vendor that says "we support Hindi" should be asked which locale the product actually runs, because a prescription written in Devanagari is a different product from one that copes with Hindi words inside an English sentence.

How does it handle Hinglish?

Most OPD speech in north and central India is mixed: medicine names and doses in English, instructions in Hindi. "Metformin 500 khane ke baad, do baar" is one sentence in two languages. An English (India) model treats this as English with unfamiliar words, so the drug names and numbers usually survive and the Hindi words come out as approximate Roman spellings. A Hindi (India) model does the reverse, and tends to mangle the English drug names. Eka Care's developer page, read the same day, says its own medical speech models transcribe "code-mixed doctor–patient conversation", which is the vendor acknowledging that the mixed case is the normal case. Whatever the vendor claims, test with your own sentences: three medicines, a dose, a frequency and a Hindi instruction, spoken at normal speed over clinic noise. Our post on voice entry of medicines in Indian English and Hinglish walks through that test.

Does it apply to a small clinic in India?

Yes, and the smaller the clinic the more it should prefer extraction over generation. A doctor seeing sixty patients a day has no time to proofread a generated paragraph per visit, but can glance at a list of five medicine names and tick them. Three practical rules follow. Use voice for bounded tasks with a short review, such as building the clinic's medicine list, rather than for the whole note. Never let voice fill a field the doctor must decide, such as whether a drug is Schedule H. And check where the audio goes: the speech vendor is a Data Processor for the clinic, so ask whether clips are stored and for how long.

Where Vitels stands

Vitels uses voice for one task: adding medicines to the clinic's list. A doctor or clinic admin records a voice note of up to 60 seconds; Azure AI Speech transcribes it in Indian English (locale en-IN), which copes with Hinglish phrasing but is not Hindi speech recognition; Claude keeps only the medicine names from the transcript, and the person edits the text, then reviews one card per name before anything is saved. The prescription status (Rx or OTC) is always chosen by the person from the pack label. Audio is not stored by Vitels, and no patient data is sent to Azure or Anthropic. There is no dictation of prescriptions or consultation notes. See add medicines with AI.