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Pemr

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A local-first, family-scale medical record framework that provides a Python CLI engine and MCP server for ingesting, deduplicating, querying, and generating med

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Описание

A local-first, family-scale medical record framework that provides a Python CLI engine and MCP server for ingesting, deduplicating, querying, and generating medical records from source documents.

README

Personal EMR — a local-first, family-scale medical record framework. Source documents (scanned labs, visit notes, etc.) are retained as-is; a SQLite database is the source of truth for structured data. Deterministic work — ingest, deduplication, query, analysis, and brief-generation — lives in a Python CLI engine wrapped by a thin MCP server, so AI agents call typed tools instead of re-inventing the logic on every request.

⚠️ This repository is framework + documentation only. No personal or medical data lives here. The live database, source scans, generated exports, and backups all reside outside the repo in a local data directory. .gitignore hard-blocks databases, documents, and the data/ inbox/ sources/ exports/ backups/ dirs as a backstop.

What it does

  • Ingest without duplication — content-hash on source files (catches re-scans) plus semantic dedup keys on extracted rows (same clinical fact from two documents → one row).
  • Query fast — canned + ad-hoc reads over a typed schema (labs, meds, procedures, appointments) with a generic observations catch-all for the long tail.
  • Generate on demand — master health summary, per-appointment "walk-in readiness" briefs, and a chronological journal, all rendered from the DB so they never drift.
  • Extend to the whole family — one DB, person_id on every row; adding a member is one command, not a fork of the tooling.

Full design — schema, dedup algorithm, ingest pipeline, tool surface, backup — in docs/Architecture.md.

Design decisions

Area Choice
Structured store SQLite (source of truth); source scans retained on disk
Schema Hybrid — typed tables + generic observations
Multi-person Single DB, person_id everywhere
Generated docs Rendered views from the DB (disposable)
Ingestion Agent does vision→structure; tools validate + dedup + commit
Interface Python CLI engine + thin MCP wrapper
Dedup Content-hash (documents) + semantic keys (rows)
Backup VACUUM INTO snapshot → cloud-synced folder; live DB stays local

Status

Pre-implementation. Design is locked; build proceeds in phases (skeleton → ingest/dedup → query → render → MCP → backup → care-gap rules) per the Architecture doc.

Data / privacy posture

Local-first. The live pemr.db sits on a non-synced local path (WAL sidecars corrupt under cloud sync); only clean VACUUM INTO snapshots sync to Drive/OneDrive. Private-ish, not encrypted-at-rest by default — an encrypted-snapshot upgrade is a drop-in later. No HIPAA/PHI compliance layer and no provider interoperability; this is a personal archive, and it assists appointment prep and research — it does not give clinical advice.


Framework scaffolding (from the template)

This repo was generated from meridun/model-repo and carries its documentation-tier system, token-optimizer hooks, role-based model routing, and the agentic SDLC pipeline. See docs/Documentation.md and docs/Development_AgenticSDLC.md. The proj- skill/agent prefix is still the template default and will be renamed to pemr- as the app takes shape.

from github.com/meridun/pemr

Установка Pemr

У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.

▸ github.com/meridun/pemr

FAQ

Pemr MCP бесплатный?

Да, Pemr MCP бесплатный — установка в пару кликов через Unyly без оплаты.

Нужен ли API-ключ для Pemr?

Нет, Pemr работает без API-ключей и переменных окружения.

Pemr — hosted или self-hosted?

Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.

Как установить Pemr в Claude Desktop, Claude Code или Cursor?

Открой Pemr на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.

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