Vascular surgery patient record system

About project

The Vascular Surgery Patient Record System operates within the closed network of the Scientific Research Institute - Regional Clinical Hospital No. 1 named after Professor S. V. Ochapovsky in Krasnodar. The system was developed to standardize and systematize vascular research results for patients in the Vascular Surgery Department.

This system automates data collection, analysis, and reporting, helping specialists efficiently process large volumes of medical information and improve patient treatment outcomes.

Facts

Secure and restricted access within the closed hospital network.
Automates measurement and analysis of vascular indicators.
Enhances diagnostic accuracy through automated calculations.
Provides configurable treatment recommendations without coding.
Supports exportable reports in *.xlsx format.

Customer

Scientific Research Institute - Regional Clinical Hospital No. 1 named after Professor S. V. Ochapovsky, Krasnodar.

Spellsystems Role

The system is designed to improve the accuracy of vascular diagnostics and streamline patient data management by automating key processes, including measurement, classification, and treatment recommendation generation.

SpellSystems played a critical role in the development and deployment of the system, including:

  • System architecture and development.
  • Backend and frontend implementation.
  • Database design and optimization.
  • Integration of automated medical measurement tools.
  • Deployment using Docker.
  • Configuration of user access and security protocols.

Goal

The system is designed to improve the accuracy of vascular diagnostics and streamline patient data management by automating key processes, including measurement, classification, and treatment recommendation generation.

Implementation

Patient Card

  • Stores CT and MRI images and videos.
  • Features built-in measurement tools (rulers, protractors) for vessel thickness and bifurcation angle analysis.
  • Automatically determines diagnosis and suggests treatment recommendations.
  • Supports non-code configuration of classification and treatment options.
Automatically Measured Parameters

  • Bifurcation angle
  • Common carotid artery (CCA) diameter
  • Internal carotid artery (ICA) deviation angle
  • ICA diameter
  • Bulb diameter
  • Bulb type
Automatically Calculated Indicators

  • Delta (D CCA - D Bulb)
  • Delta (D Bulb - D ICA)
  • Delta (D CCA - D ICA)
  • Ratio (D Bulb - D ICA)
Classification and Treatment Options

  • Enables doctors to configure patient classification criteria.
  • Auto-assigns diagnoses based on measured indicators.
  • Provides treatment recommendations without requiring code changes.
Patient Registry

  • Comprehensive patient record management.
  • Functions include adding, modifying, deleting, and archiving records.
  • Advanced filtering for quick data retrieval.
  • Export functionality in *.xlsx format.
Administration Module

  • Allows department administrators to create and manage user accounts.
  • Logs user authorization and tracks modifications.
  • Ensures compliance with data security policies.

Technologies

Basic technologies
1
  • Python
Storing and working with data
2
  • PostgreSQL
Web Interface
3
  • React.js
Infrastructure
4
  • Docker