AI Scribe for Hindi, Hinglish & 20+ Indian Languages

An AI medical scribe built for India has to do something most global scribes don't even attempt. It has to understand a sentence that starts in Hindi, switches to English for the drug name, and finishes in Hindi again, then write the note in clean clinical English. AI Scribe by Patient Square captures English, Hindi, and 20+ Indian languages, code-mixing and all, and returns a structured note you can put straight into a referral. Here's how that works, and why it matters.

Key takeaways

• You and your patient speak Hindi, Hinglish, or a regional language. The note always comes out in clean clinical English.
• Code-switching mid-sentence is a known weak point for ordinary transcription tools, which are trained on one language at a time.
• Several global scribes publish language lists that name no Indian language. The gap is real.
• Capture works offline with on-device encryption for low-connectivity clinics, and audio is never stored.

What does a real Indian consult actually sound like?

Not textbook Hindi, and not textbook English. It sounds like this. The exchange below is an illustration, not a real patient:

Doctor: "Toh sugar ka kya scene hai? Last HbA1c kitna tha?" Patient: "Doctor sahab, woh 8.2 tha. Lekin ab thoda better feel ho raha hai. Metformin le raha hoon regularly." Doctor: "Theek hai. BP bhi check karte hain. Aap evening walk kar rahe ho? Diet mein sugar control rakhna hai. Main metformin continue rakhta hoon, aur ek SGLT2 inhibitor add karte hain."

Three sentences, four language switches, two drug classes, a lab value, and a plan, all braided together the way medicine is actually practiced in a Gurgaon or Pune OPD. A general Hindi transcriber would garble "HbA1c", "metformin", and "SGLT2 inhibitor." A general English transcriber would drop the Hindi entirely. Neither gives you a usable record.

What does the note look like afterward?

Clean clinical English, structured, ready to sign:

S: Type 2 diabetes follow-up. Reports feeling better. Adherent to metformin. Previous HbA1c 8.2%. O: BP to be recorded. Lifestyle: advised evening walks, dietary sugar control. A: Type 2 diabetes mellitus, suboptimal glycemic control on metformin monotherapy. P: Continue metformin. Add SGLT2 inhibitor. Reinforce diet and exercise. Review with repeat HbA1c.

The patient spoke Hindi. The chart speaks English. That inversion is the whole point, and it's the part that makes the record useful to a referral specialist, an insurer, or a court that won't read Hinglish.

Why is the note in English when the visit was in Hindi?

Because the record has a different audience than the conversation. The conversation belongs to you and your patient, in whatever language puts them at ease. The record goes to people and systems that work in English: the cardiologist you refer to, the TPA processing the cashless claim, the medico-legal file if a case is ever raised. A Hindi transcript, which is all most speech tools produce, is not a medical record. A clean English note is.

This is also where ordinary transcription tools and an ambient scribe part ways. Tools like generic speech-to-text give you a wall of text in one language. AI Scribe by Patient Square is an ambient AI medical scribe that listens during the visit and hands back a structured SOAP note, ICD-10 suggestions, and a prescription draft, ready to review and sign about two minutes after the visit. The structuring and the translation happen together.

Why is Hinglish so hard for ordinary tools?

Code-switching, the act of changing languages within a single utterance, is a recognized hard problem in speech recognition. Most models are trained predominantly on one language, so when a speaker flips mid-sentence, accuracy on the "other" language drops sharply. Research on Hindi-English code-switched speech, including efforts to build dedicated code-switched corpora, exists precisely because off-the-shelf monolingual models handle it poorly. The failure lands exactly where medicine can't afford it: the English drug name embedded in a Hindi sentence, the lab abbreviation, the dose.

That's why "supports Hindi" on a vendor's page isn't the same as "handles a real Hinglish consult." Supporting Hindi audio and supporting a doctor who says "BP thoda high hai, metformin continue rakhiye" are different engineering problems. We built for the second one.

How do the global scribes compare on language?

A fair word on the others: eka.care genuinely names a deep list of Indian languages. It's India-native and credible on this axis, and if you want a closer look the India scribe comparison lays us side by side. The global vendors are the real mismatch. A page claiming "90+ languages" that names not one Indian language is borrowing authority it hasn't earned for an Indian consult.

Why this matters in a two-minute OPD

A BMJ Open review covering 28.5 million consultations across 67 countries put India's average primary-care consultation at about two minutes, among the shortest measured anywhere. At one government super-speciality hospital, several OPDs each saw more than 200 patients a day. There is no version of that day where you also type a structured English note from a Hindi conversation. Either the record gets written by something that listens while you work, or it doesn't get written well.

For clinics where the internet is unreliable, capture works offline with on-device encryption and syncs later, so a power cut or a dead connection doesn't cost you the note. And the visit audio is never stored. It's processed in memory and discarded once the note is drafted, which is also the right answer under the DPDP Act. The full posture is on our security page.

The honest test is your own patients. Take the 7-day trial, no card needed, and run it on a real OPD morning, Hinglish and all. Read the English notes it produces and decide whether they're ready to sign. If you're weighing the price first, the India rate card shows every number in rupees, GST included.