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NHS Trawler Python Project

Documenting the NHS Trawler Python Project

Overview

The NHS Trawler project is a Python-based application designed to scrape and curate data from various NHS-related websites. The goal is to create a user-friendly interface that allows users to quickly access and understand NHS services, statistics, and other relevant information. From the outset, the project was intended to be a learning experience, both for myself and for my grandson, who is interested in applying to medical school. The idea is to help him gain a better understanding of the NHS and its structure, issues, and services. For me it was a refresher in Python and data scraping, as well as an opportunity to explore the vast amount of data available on the NHS websites. Also, to gain more experience with AI tools and techniques. There is no doubt that this project has been a collaborative effort, with significant contributions from AI tools like Copilot and Claude. These tools have helped speed up the development process and provided valuable insights into Python development and data scraping techniques.

Why This Project?

The NHS Trawler project was initiated to address the challenge of accessing and understanding the vast amount of NHS-related information available online in a timely manner. By aggregating and curating this data, the project aims to provide users (mainly my grandson) with a comprehensive and easily navigable resource for NHS services, statistics, and news. This came about as he was looking to apply to medical school and I thought it would be a good idea to help him get a better understanding of the NHS and its structure, issues, and services.

Project Goals

  • Data Scraping: Use Python libraries like requests and BeautifulSoup to scrape data from NHS-related websites.
  • Data Curation: Organize and curate the scraped data to make it easily accessible and understandable. In other words, to make it easier to find the information you are looking for quickly in easily digestible formats. For an example of this, see the report so far.

Project Structure

The project is structured to break down tasks into manageable components, each focusing on a specific aspect of the data scraping and curation process. Building the project using mainly Claude was an iterative process, with frequent adjustments and improvements based on testing and feedback. Claude had a habit of building all functions into a single file, which I found to be a bit cumbersome, so refactoring the code into a more modular structure as we went along was a key part of the development process. This modular approach not only made the code more readable and maintainable but also allowed for easier testing and debugging.

Comment on the process

The development process for the NHS Trawler project was highly iterative, with a strong emphasis on testing and feedback. By leveraging AI tools like Claude, I was able to quickly prototype and refine the approach to data scraping and curation. The modular structure of the codebase facilitated easier testing and debugging, allowing us to identify and address issues more efficiently. Overall, the project served as a valuable learning experience, highlighting the importance of collaboration and adaptability in software development. Claude did tend to bloat the code with lots of separate test scripts, but hey ho must be good to do lots of testing, right? I have been trying to get better at documenting my work, so I should take a leaf out of Claude’s book as it did a good job of that, even if it was a bit over the top at times.

Features:

  • Gmail SMTP integration with app passwords
  • Automated batch execution via Windows scripts
  • Comprehensive error logging and monitoring
  • JSON persistence for historical tracking

💡 Technical Highlights

  • Adaptive Scraping: Multiple fallback strategies for different HTML structures
  • Modular Design: Clean separation enabling easy maintenance and extension
  • Error-First Architecture: Graceful degradation when sources are unavailable
  • Configuration-Driven: Easy deployment across different environments

Intelligent Content Processing

There was always a need to ensure that the content was balanced and relevant and that each source was appropriately represented. The project includes a function to distribute content evenly across different categories, such as guidelines, newsletters, NHS news, and NHS Digital updates. This ensures that users receive a well-rounded view of the NHS landscape without overwhelming them with too much information from any single source. Also, the system is designed to adapt to user preferences over time, learning which types of content are most engaging and adjusting the distribution accordingly.

def get_balanced_content(max_items=50):
    # Distributes content: 40% guidelines, 20% newsletters,
    # 25% NHS news, 15% NHS Digital
    return distribute_content_evenly(guidelines, newsletters, news, digital)
    # 25% NHS news, 15% NHS Digital
    return distribute_content_evenly(guidelines, newsletters, news, digital)

Data Sources

The project currently scrapes data from six primary sources:

  • NICE: Provides clinical guidelines and health technology assessments.
  • NHS Digital: Offers statistics and data on NHS services.
  • NHS England: Focuses on health and care services in England.
  • Public Health England: Provides data on public health and epidemiology.
  • NHS News: Aggregates news and updates from the NHS.
  • NHS Trusts: Local organizations responsible for providing NHS services.

Future Plans

The NHS Trawler project is still a work in progress, with no plans currently to expand its capabilities.