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Overview

This documentation project has been crafted to offer a comprehensive introduction to a wide array of open-source technologies that can be leveraged across the NHS. These technologies range from app development, data analytics, and visualization, to project management, and even extend into advanced fields like artificial intelligence and machine learning.

The NHS is a complex ecosystem composed of various organizations, each with varying degrees of familiarity with open-source tools. Therefore, this project aims to consolidate technical resources, guides, and user experiences to facilitate easier adoption and implementation. Often, organizations can find the array of choices daunting and may revert to costly paid services with significant licensing fees. This guide seeks to demystify open-source options, presenting them as viable, cost-effective alternatives that can drive innovation and efficiency within the NHS. By doing so, it hopes to empower organizations to make informed decisions and encourage a more collaborative and resourceful approach to technology in healthcare.

You will be able to find further information about all of the following items in the lefthand side menu:

  1. Programming Languages

    • Python: A versatile language ideal for scripting, data manipulation, and building complex applications. Learn more about Python.
    • R: Specialized in statistical computing and graphics, R is widely used in data analysis and healthcare research. Explore R resources.
    • SQL: Your existing knowledge in SQL will be beneficial across multiple databases and applications. Deepen your SQL skills.

  2. Web Development Frameworks

    • React & React Native: Create dynamic web and mobile applications with these JavaScript libraries. Start with React.
    • Angular & Vue.js: Robust frameworks for building user interfaces and single-page applications. Discover Angular | Get started with Vue.js.
    • Express.js & Next.js: Server-side frameworks for building web applications and SSR in Node.js environments. Learn Express.js | Explore Next.js.
    • Remix.js & Svelte: Modern tools for building better web apps faster with less boilerplate. Try Remix.js | Learn Svelte.

  3. Data Visualization Tools

    • Dash & Shiny: Powerful tools to create interactive web applications for data analysis directly from Python and R. Explore Dash | Discover Shiny.
    • Evidence.dev: Build lightweight, scalable, and collaborative data apps with SQL & Markdown. Get started with Evidence.dev.

  4. Version Control & Collaboration

    • Git & GitHub: Master these tools to enhance your code management and collaborate on projects. Learn Git basics | Explore GitHub.

  5. Development Tools & Platforms

    • Jupyter Notebooks: Ideal for prototyping and interactive data science tasks. Start with Jupyter.
    • Docker: A platform for developing, shipping, and running applications in containers. Learn Docker.
    • Redash: Connect and query your data sources, visualize results and share your insights. Explore Redash.

  6. IDEs

    • Visual Studio Code (VSCode): Set up your development environment with this lightweight, powerful source code editor. Setup VSCode.

  7. AI & Machine Learning

    • Introduction to AI: Understand the basics of AI and its applications in healthcare. Learn about AI.
    • Machine Learning Tools: Explore various frameworks and libraries including TensorFlow, PyTorch, and more. Discover ML tools.
    • ChatGPT: Learn how conversational AI can enhance healthcare services. Explore ChatGPT.

Quickstart​

  1. Install Node.js & Git
  2. Install your IDE i.e. VSCode / RStudio
  3. Install R / Python / SQL
  4. Learn basics of R / Python / SQL
  5. Utilise ChatGPT / Claude to help with project ideas.
  6. Depending on project, pick Web Dev Framework / Data Vis Tool / Dev Tool or Platform
  7. Develop your project
  8. Publish your project