As a medical physicist, you know that extracting and analyzing QA data from reports can be a time-consuming task, especially when dealing with large datasets. Whether you’re managing multiple QA tests or dealing with complex reports, manual data extraction can eat up hours of your valuable time. That’s why I’ve developed a Python tool to automate the extraction of QA data from PDF reports—saving you time and reducing the risk of human error.
What’s the Problem?
QA reports are a key component in the medical physics workflow, but often, they come in PDF format. Extracting key data from these reports, such as distance, percentage differences, and pass/fail status, can be cumbersome. For medical physicists working with SNC patient QA reports, manually sorting through these PDFs is not only inefficient but also prone to mistakes.
The Solution: A Python Tool for Automation
I developed this tool to automate the process of extracting QA data from PDFs. Using pdfplumber, a reliable Python package for extracting text from PDF files, this tool is designed to handle two SNC patient QA report formats. With it, you can automatically process all PDF files within a designated root folder, including any subfolders. The tool will extract essential QA data such as:
- Distance (mm)
- Difference (%)
- Pass/Fail Status
- Pass/Fail Points
- Total Points
By automating this process, you can drastically reduce the time spent on manual data entry and focus more on critical analysis.
How Does It Work?
The tool works seamlessly by scanning all PDF files within a main folder and extracting the necessary data from each report. It can handle multiple reports at once, which makes it ideal for labs or clinics with large sets of QA data. You no longer have to open each report individually and extract the data by hand—this tool does it all in a matter of seconds.
Why You Need This Tool
- Efficiency: Automatically processes all PDFs within a folder, saving you countless hours of manual work.
- Accuracy: Reduces the chance of human error, ensuring that your extracted data is reliable and accurate.
- Versatility: Supports multiple SNC patient QA report formats.
- Time-Saving: Extracts key QA data in seconds—ideal for large datasets.
Watch the Demo Video
I’ve also created a demo video where I walk you through how the tool works and show you how easy it is to use. Check it out below for a detailed walkthrough:
Pricing and Options
I offer the tool in two pricing tiers to fit your needs:
- Standalone Product + Email Support: $19.99
This option includes the tool and access to email support for any questions or issues you may have. - Product + Ongoing Help (1 Year of Support): $49.99
This option includes the tool along with one year of ongoing support. You’ll have access to personalized help whenever you need it, without worrying about any additional charges.
Ready to Get Started?
The tool is available for download and can be set up quickly. If you’re tired of spending hours extracting data from PDFs, this tool is for you. Click the link below to get your copy today:
Don’t forget to leave any questions or feedback in the comments section. I’m excited to help you streamline your QA process and improve your workflow.
Conclusion
If you’re looking for a way to save time, reduce errors, and increase efficiency in your QA process, this Python tool is the perfect solution. By automating the extraction of data from PDF reports, you can focus on what matters most—ensuring the quality and accuracy of your treatments.
Thanks for reading, and happy automating!