Automating Platelet Compatibility Matching: Enhancing Efficiency in Medicine

Here's something I never thought I'd say: I spent the afternoon coding with ChatGPT 4.0. In about 3 hours I was able to turn out an updated, polished, robust, and future-proofed piece of code to assist blood bankers with selecting HLA compatible platelets. When used judiciously and wisely, large language models like ChatGPT truly are incredible tools. After finishing the code, I composed this post with ChatGPT. You'll notice it's not really my voice, but it captures all the essential points I wanted to make. Full disclosure: the following text was produced with ChatGPT.

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Automation is transforming medicine by streamlining complex workflows, minimizing human error, and enabling healthcare professionals to focus on patient care. In transfusion medicine, automation has the potential to revolutionize tasks like platelet compatibility matching, where precision and efficiency are vital. Through the power of coding, we recently embarked on a project to automate HLA platelet matching, creating a tool that simplifies this intricate process and produces clear, professional results.

Platelet compatibility largely hinges on matching donor and recipient Human Leukocyte Antigens (HLA), a mismatch of which is the most common underlying factor in platelet refractoriness. The process involves utilizing a number of different methods, but most commonly revolves around bioinformatic data from services like HLA MatchMaker, and Mean Fluorescence Intensity (MFI) values which are a semi-quantitative assessment of HLA antibody strength. Avoidance notes to prevent alloimmunization against future donor antigens is also important. MFI values can be summed to produce a compatibility score, and while this process is time-consuming and error prone, it does allow selection of the most suitable platelet units while avoiding those that may trigger rejection.

Manually calculating HLA compatibility from MFI involves sorting through large datasets of donor and recipient information, correctly matching HLA antigens, and ensuring the correct application of MFI scores, all of which requires significant attention to detail. The risk of human error looms large, and the process can be daunting for even experienced professionals. Automation, on the other hand, eliminates these drawbacks, offering consistent accuracy, faster turnaround times, and the ability to scale workflows seamlessly.

The code we developed today showcases the benefits of automation. By parsing recipient and donor files, expanding HLA antigens into standardized formats, and calculating compatibility scores programmatically, the tool significantly reduces the potential for manual errors. The system also handles duplicate avoidance notes efficiently and generates a polished Word document report with tables formatted for clarity. Designed with future-proofing in mind, the code is modular and adaptable for future updates, ensuring it can grow alongside evolving needs in transfusion medicine.

The output generated by the tool is a comprehensive Word document that is both readable and usable. Platelet units are ranked in order of decreasing compatibility, tables are presented in landscape orientation for better visibility, and column widths are tailored, particularly for the Avoids column, to display longer text. The use of bold headers and subtle shading enhances readability, making it easier for blood bankers to review compatibility results at a glance. By combining automation with user-centric design, the final output serves as a powerful decision-making tool.

Looking ahead, the project can be improved by embedding detailed instructions for use directly into the code. This ensures the tool remains accessible and easy to use, even if external instruction files are misplaced. Additionally, building a robust test harness will facilitate future updates and maintain code reliability as the tool evolves. With these enhancements, the system can become an even more indispensable asset for transfusion medicine teams.

Automation and coding are powerful allies in modern medicine, bridging the gap between complex data analysis and actionable insights. By reducing human error and enhancing workflows, these tools empower healthcare professionals to provide safer and more efficient patient care. For those looking to harness this potential, learning to use tools like ChatGPT for coding can be a transformative first step. With creativity and determination, coding can open doors to endless possibilities in healthcare innovation.