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Accelerator 03 · Healthcare

Clinical notes to structured summary

Turns a doctor's shorthand notes into a discharge summary, suggested codes for coder review and a follow-up plan.

The problem

  • Discharge summaries are typed late in the shift from shorthand notes, so they are delayed and inconsistent.
  • Coders re-read the full chart to assign diagnosis codes for billing and insurance.
  • Follow-up instructions are often missing or not in a language the patient reads.

What it does

  • Expands abbreviations and structures the note into a standard discharge summary.
  • Suggests ICD-10 codes, clearly marked for a certified coder to confirm.
  • Lists medications with dose, frequency and duration, and the follow-up schedule.
  • Keeps patient data on the hospital's own server when deployed on-prem.

Pick a synthetic doctor's note and get a discharge summary, suggested codes and follow-ups.

Live demo: Clinical notes to structured summary

Synthetic data only · nothing you type is stored

1. Pick a sample

2. Review or edit the input

723 / 6,000

Please do not paste real personal, patient, customer or confidential data. Use the samples or your own synthetic text.

Output

Discharge summary, suggested ICD-10 codes (for coder review), medication list and follow-ups.

The demo first calls the Surge in-house AI server and falls back to a hosted open-source model if it is busy. The badge on the output shows which one answered. Limited to a few runs per visitor per day.

How it integrates

In your deployment the accelerator runs inside your environment, behind your single sign-on, and is connected to the systems below. You choose the hosting and the model.

Systems and APIs

  • HIS / EMR via HL7 v2 or FHIR (DocumentReference, Encounter)
  • Coding and billing module
  • Patient app / SMS for follow-up instructions

Data we need for a pilot

  • Discharge summary template
  • De-identified sample notes from 2–3 departments
  • Hospital abbreviation list

Where it runs

  • On-prem only for identifiable patient data
  • Synthetic or de-identified data for cloud pilots

Open-source models we start with

  • Qwen3-class 14B–32B instruct model, or a medically tuned open model after evaluation
  • IndicTrans2 for patient instructions in Indian languages

Walkthrough

A short recorded walkthrough of the clinical notes to structured summary: the input, the output and how a reviewer uses it.

Next accelerator: Regulatory & literature research assistant →

Walkthrough video coming soon

We are recording a short walkthrough of this accelerator. Meanwhile, run the live demo above or book a working session.

Run the healthcare accelerator on your data

In a working session we map it to your process, systems and rules, and agree what a pilot must prove before it goes live.