In Kevin B. Johnson's Lab, we embarked on a groundbreaking research endeavor focused on automating patient portals using advanced machine learning algorithms. Leveraging the state-of-the-art natural language processing capabilities of OpenAI's GPT-based models, we developed a robust, multi-step pipeline designed to enhance patient-doctor communication. The project aims to sift through patient messages and provide accurate, contextually relevant responses, thus significantly reducing the time healthcare providers spend on routine inquiries. Not only does the automation serve as an adjunct to healthcare providers, but it also improves the timeliness and value of the information available to patients.

Our methodology encompasses several stages, each contributing to the final output's quality and reliability. We began with pre-processing techniques to filter and categorize patient messages, which were then fed into a fine-tuned GPT model trained on a custom dataset. This approach enabled the system to generate human-like responses that are medically accurate and context-sensitive. As we strive to perfect this technology, we continue to assess its efficacy through rigorous testing and iterations, hoping to revolutionize the way healthcare providers and patients interact in a digital age.