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\u2014for 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.\nI know I might get backlash over AI use but I am still learning GO. (I am from JavaScript background)"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Clinglang \u2013 A shorthand language for doctors to write structured cases"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://github.com/ppnpm/clinlang"}},"_tags":["story","author_ppnpm","story_48333114","show_hn"],"author":"ppnpm","created_at":"2026-05-30T06:03:22Z","created_at_i":1780121002,"num_comments":0,"objectID":"48333114","points":1,"story_id":48333114,"story_text":"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\u2014for 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.\nI know I might get backlash over AI use but I am still learning GO. (I am from JavaScript background)","title":"Show HN: Clinglang \u2013 A shorthand language for doctors to write structured cases","updated_at":"2026-05-30T06:06:54Z","url":"https://github.com/ppnpm/clinlang"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"westurner"},"comment_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["clinical","decision","support","agent","ai","medical"],"value":"Yeah, so it's at that workflow where they're not getting linked data edges out of their own unstructured data input.
To prompt them at that time for whether or not this referenced thing in a dictated .txt file is actually a thing with a URI and have them confirm that those are the correct annotations for their input would save a lot of time and money.
Requisite study for Linked Data clinical coding / informatics:
- HIPAA and unstructured notes, HIPAA and Linked Data; Informed Consent; Precision Medicine
- Tokens; NLP, stemming, conceptual entity recognition
- The LODcloud; a great big graph of Linked Open Data (that our data does not yet link to, is siloed separately from, does not yet have references to existing URIs in)
- RDFa: RDF-in-html-Attributes
- JSON-LD: JSON Linked Data
- Schema.org/MedicalEntity ; /docs/schemas.html -> "Health and medical types" https://schema.org/docs/meddocs.html
- TTS Text-to-Speech, Speech-to-Text; Multimodal models with RAG, LORA, RLHF, Transformer Self Attention tensor Networks, Benchmarking OpenAI Whisper with relative performance metrics, ONNX
- FHIR for EHR data portability (JSON-LD,)
UX Improvements:
- More IDE-like unstructured data entry
- Auto-annotate this text field in place
- Auto-annotate this pasted text
- Auto-annotate this text field and display the linked data annotations
- Display the revisions of the unstructured note -> annotated linked data document
- Autocomplete with search from e.g. icd:
- Frequently used annotations per Clinician/Provider
- Recently used annotations per Clinician/Provider
- Hover over visually distinguished auto-recognized entities and approve/clarify/reject
- Indicate which prov:Agent added which annotations; human (name(s)), AI (signed git revision of repo URI)
- Indicate degree of confidence in annotation (note that AGI hypergraph systems have TruthValue and also AttentionValue, like attention networks; and that AGI systems may someday also support CDSS: Clinical Decision Support Systems and Health Decision Support Systems)"},"story_title":{"matchLevel":"none","matchedWords":[],"value":"Snomed CT Entity Linking Challenge"},"story_url":{"matchLevel":"none","matchedWords":[],"value":"https://www.drivendata.org/competitions/258/competition-snomed-ct/"}},"_tags":["comment","author_westurner","story_38739890"],"author":"westurner","children":[38744686],"comment_text":"Yeah, so it's at that workflow where they're not getting linked data edges out of their own unstructured data input.
To prompt them at that time for whether or not this referenced thing in a dictated .txt file is actually a thing with a URI and have them confirm that those are the correct annotations for their input would save a lot of time and money.
Requisite study for Linked Data clinical coding / informatics:
- HIPAA and unstructured notes, HIPAA and Linked Data; Informed Consent; Precision Medicine
- Tokens; NLP, stemming, conceptual entity recognition
- The LODcloud; a great big graph of Linked Open Data (that our data does not yet link to, is siloed separately from, does not yet have references to existing URIs in)
- RDFa: RDF-in-html-Attributes
- JSON-LD: JSON Linked Data
- Schema.org/MedicalEntity ; /docs/schemas.html -> "Health and medical types" https://schema.org/docs/meddocs.html
- TTS Text-to-Speech, Speech-to-Text; Multimodal models with RAG, LORA, RLHF, Transformer Self Attention tensor Networks, Benchmarking OpenAI Whisper with relative performance metrics, ONNX
- FHIR for EHR data portability (JSON-LD,)
UX Improvements:
- More IDE-like unstructured data entry
- Auto-annotate this text field in place
- Auto-annotate this pasted text
- Auto-annotate this text field and display the linked data annotations
- Display the revisions of the unstructured note -> annotated linked data document
- Autocomplete with search from e.g. icd:
- Frequently used annotations per Clinician/Provider
- Recently used annotations per Clinician/Provider
- Hover over visually distinguished auto-recognized entities and approve/clarify/reject
- Indicate which prov:Agent added which annotations; human (name(s)), AI (signed git revision of repo URI)
- Indicate degree of confidence in annotation (note that AGI hypergraph systems have TruthValue and also AttentionValue, like attention networks; and that AGI systems may someday also support CDSS: Clinical Decision Support Systems and Health Decision Support Systems)","created_at":"2023-12-23T13:43:47Z","created_at_i":1703339027,"objectID":"38744177","parent_id":38740869,"story_id":38739890,"story_title":"Snomed CT Entity Linking Challenge","story_url":"https://www.drivendata.org/competitions/258/competition-snomed-ct/","updated_at":"2024-09-20T16:00:01Z"}],"hitsPerPage":20,"nbHits":2,"nbPages":1,"page":0,"params":"query=clinical+decision+support+agent+AI+medical&advancedSyntax=true&analyticsTags=backend","processingTimeMS":40,"processingTimingsMS":{"_request":{"queue":43,"roundTrip":20},"fetch":{"query":31,"scanning":7,"total":39},"total":40},"query":"clinical decision support agent AI medical","serverTimeMS":84}