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Shorthand writing for doctors. Write shortly and later convert into structured case documentations.

2 starsHTML

Clinglang – A shorthand language for doctors to write structured cases

by ppnpm·May 30, 2026·1 point·0 comments

AI Analysis

●●SolidNiche GemBig Brain

Parses zero-punctuation medical shorthand into SOAP notes faster than voice AI for noisy hospitals.

Strengths
  • Self-contained Go binary embeds React frontend allowing single-file zero-dependency deployment on any server.
  • Specialty plugins like OBGYN metrics extend core parsing logic easily without requiring changes.
  • Local-first design avoids HIPAA concerns associated with cloud-based ambient AI scribes and vendors.
Weaknesses
  • Manual shorthand requires learning curve versus passive ambient voice recording tools used elsewhere.
  • AI-generated codebase may raise concerns about long-term maintainability and depth within the project.
Category
Target Audience

Doctors, medical residents, clinical students

Similar To

Nuance DAX · Ambience · TextExpander

Post Description

I am building a language, parser, and a simple editor to write this language.

It is a simple shorthand, command- and token-based domain-specific language that may help doctors and clinicians write structured clinical case notes and histories faster.

It will convert these short notes into SOAP notes (currently available), and later into Markdown, JSON, and PDF.

I do not intend to make it a clinical decision support tool, as that would impose many restrictions.

I am positioning it as a note-taking tool for doctors, medical students, and residents.

I understand that ambient AI tools are now available for faster case documentation, but they may not be usable everywhere, especially in busy and noisy hospital environments.

I am using AI agents to write most of the code, while I supervise the app architecture, editor UI, and the system for easily adding plugins.

Regarding plugins, I plan to implement them as additional commands and tokens specific to departments—for example, obstetrics and gynecology-specific commands, and ICU-specific commands.

I have implemented the parsing engine in Go, and the front end is built using React + Vite for the web application.

It is still incomplete and highly bloated with AI slop (yes I accept that) but once I make the satisfying version. I am willing to do the trimming later on.

You can take a look at the code here : https://github.com/ppnpm/clinlang

I am open to suggestions, feedback and criticism. I know I might get backlash over AI use but I am still learning GO. (I am from JavaScript background)

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