FDA applies machine learning to streamline drug safety reviews
Our cloud-native machine learning prototype will help the Food and Drug Administration's Center for Drug Evaluation and Research explore the use of advanced technologies for efficient and timely review of drug products for the U.S. market.
The Division of Medication Error Prevention and Analysis (DMEPA) within FDA's Center for Drug Evaluation and Research reviews pre-market and post-market drug labeling to minimize the risk of medication errors. Working with their team, we are developing a cloud-native machine learning prototype that uses models, algorithms, and machine vision to streamline and minimize inefficiencies in the drug labeling review process.
Challenge
FDA’s drug labeling review process is both extensive and time sensitive. Typically, each medication error reviewer performs 25 to 50 drug pre-market reviews per year, analyzing elements such as a label’s font size and its ability to accurately meet regulations and standards. The goal of such a detailed review process is to ensure a drug product is safe and effective and prevent accidents—misinterpreted labels can lead to adverse reactions or even accidental death.
The review process, which includes back-and-forth communication between reviewers and drug manufacturers, is often manual and, at times, may be subjective by individual reviewers. FDA sought to develop a cutting-edge technology solution to expedite, standardize, and streamline the user experience for medication error drug labeling reviewers, ultimately benefiting all healthcare.
- AI
- Cloud
- Human-centered design
- Scaled Agile
- Open source
Solution
We are collaborating with FDA reviewers to build out a machine learning prototype known as the Computerized Labeling Assessment Tool (CLAT). CLAT is designed to use algorithms and machine vision to read drug labels and pinpoint specific items for review. We train our machine learning models on thousands of images to ensure sensitivity and accuracy. Simultaneously, we tap into the expertise of our data scientists to develop algorithms and tune them to the problem at hand.
Our new solution delivers these key benefits:
- Increased efficiency – CLAT will streamline the review process, allowing reviewers to identify potential issues quickly and document the process in a centralized application.
- Improved accuracy – Through user feedback mechanisms, the machine learning models continuously learn and improve, enhancing error detection over time.
- Standardized practices – CLAT promotes consistent review practices by standardizing the process and encouraging the submission of high-resolution images for review and eventual publication in public repositories.
As part of our solution, we collaborated with FDA to design and build out an AWS Well-Architected Framework to support the needs of the CLAT application. CLAT is built on top of several AWS services:
- AWS Cloud services – The project uses AWS services, including Amazon DynamoDB, Amazon API Gateway, AWS Lambda, and Amazon Simple Storage Service (Amazon S3). The use of cloud infrastructure promotes adaptability and scalability as the needs of FDA change and grow.
- Machine learning technologies – Amazon Elastic Container Service (Amazon ECS) instances allow for efficient installation and running of machine learning software with open-source components such as TensorFlow and Tesseract OCR to create, train, and run machine learning models for image analysis.
Using sequential transfer learning, the computers were initially trained on unrelated, randomized images. This helped the model learn how to distinguish the important elements of an image—the objects you want it to recognize, such as a graphical symbol of an ear on a bottle of medicine intended for administration in the ear—from the unimportant elements or background. A visual representation of this can be seen below, in Figure 1.
Figure 1. Image of an ear icon being trained on randomized photo images. This is the first step of our transfer learning process when training convolutional neural networks to identify medically relevant symbols.
Where we are now
We’ve built the foundational machine learning models that will increase accuracy in identifying drug labeling errors while maintaining full compliance with industry-standard data privacy regulations throughout the project's implementation.
The initial results from the CLAT prototype are promising. Notably, unlike DMEPA reviewers, CLAT can perform multiple simultaneous checks across multiple labels. Furthermore, all results are automatically tracked, annotated, and stored in a centralized location for future review. These advancements are a significant step forward in streamlining regulatory processes and ensuring timely and accurate assessments.
The AWS Well-Architected Framework resulted in an image training methodology applicable to various object detection tasks in the healthcare field. Through our collaboration, ICF and FDA have developed a quick way to teach computers to identify healthcare-related items on drug labels.
As work to optimize the impact of CLAT advances, the FDA continues to explore how this machine learning prototype could be used in other review processes. While work on the prototype project is currently focused on the technical side, the goal is to eventually develop a friendly, approachable experience for front-end users.
This project exemplifies the power of artificial intelligence to revolutionize healthcare practices. By enhancing efficiency and accuracy in drug labeling reviews, CLAT paves the way for improved patient safety and medication use. Learn more about how we use AWS technology to help clients scale their greatest innovations.
