The U.S. Food and Drug Administration’s (FDA) multifaceted responsibilities require continuous monitoring of trends in science and technology for the advancement of public health. In early 2020, the agency saw investigational new drug (IND) applications skyrocket to 3,806 — a significant increase compared to the previous year when they received only 166 applications during the same months.1

A central part of the FDA’s TMAP blueprint is making better use of data, including stewardship, security, quality control, analysis, and use of data in developing state-of-the-art products and solutions. (Credit: MasterControl)

Much of the growth was attributed to a surge in new cell and gene therapy products. Also, combination products using advanced software and interconnectivity are becoming more popular in the point-of-care (POC) and home healthcare markets. These products are designed to provide a high level of precision with treatment regimens. They also enable healthcare providers to receive and react to data in real time, which greatly enhances many aspects of patient care. Drug-delivery system innovation is on the rise as well. Nanotechnology is currently being explored as a drug-delivery vehicle due to its ability to provide site-specific and target-oriented delivery of medicines.2

The common thread among these and numerous other advancements in healthcare is the use of advanced technologies, including connectivity, interoperability, automation, and the use of more data. In response to the upswing in new discoveries and innovation in the life sciences, the FDA implemented its Technology Modernization Action Plan (TMAP). Launched in 2019, the agency’s modernization initiative has been a groundbreaking endeavor not only to facilitate innovation, but to streamline regulation of increasingly complex products.

Oversight of technology-based medical products requires cross-functional expertise from multiple offices within the FDA. For this reason, the agency’s modernization effort allows more transparency and collaboration across the organization — as well as the overall life sciences community. To effectively expedite innovative and breakthrough products, the agency recognized that it needs to be more agile and dynamic. Therefore, a top priority of the agency’s TMAP agenda is to modernize its own technology infrastructure by migrating all of its IT functions to a cloud environment. Another objective is to drive industrywide technological progress to deliver more value to consumers and patients.3

Next Up: More Focus on Data

The TMAP provides a sturdy foundation for the FDA’s ongoing modernization strategies. According to Acting Food and Drug Administration Commissioner Janet Woodcock and former Principal Deputy Commissioner Amy Abernethy, data is the next step in the agency’s approach to modernization.

Data has always formed the basis of the FDA’s science-based regulatory decision-making. The data may come from relatively traditional sources such as measurements submitted from clinical trials or observations from FDA field inspections. As technology has become more sophisticated and the world has become more connected, data from many new sources is helping us understand how medical products are performing, how we can pinpoint the source of a foodborne illness, for example, or understand an emerging public health threat.4

Digitizing Healthcare

Data has a more central role in the development and use of medical products. For example, artificial intelligence (AI) and machine learning (ML) technologies are highly data centric and are becoming more common in mainstream healthcare. Valuable traits of AI/ML technologies include their ability to continue learning and improving through real-world use.

AI is designed to simulate human intelligence processes. It acquires information, determines how to analyze and use the information, and self-corrects upon receiving new data. An AI algorithm is programmed to continue learning and adapting based on the data it receives, making it useful for processes that involve analyzing enormous amounts of data. ML is an AI technique that can be used to design and train software algorithms to learn and act on data, which is commonly used for predictive analytics. According to the FDA’s action plan on AI and ML, the technologies have the potential to transform healthcare by deriving new and important insights from the vast amount of data generated during the delivery of healthcare every day.5

The most common application of ML in healthcare is precision medicine. This emerging approach to disease treatment and prevention considers the variability in genes, environment, and lifestyle of each person. Healthcare professionals can use precision medicine to predict which treatment and prevention strategies will work best for a particular patient. AI can be used in the research and design of personalized medicines at all relevant phases of the clinical development and implementation of new products — from finding appropriate intervention targets to testing them for their utility.6

For example, in April 2018, the FDA approved a medical device that uses AI technology for detecting a greater than mild level of eye disease. The device, called IDx-DR, is a software program that uses an AI algorithm to analyze images of the eye taken with a retinal camera. The program reveals one of two results to the healthcare provider: (1) “More than mild diabetic retinopathy detected: refer to an eye care professional.” (2) “Negative for more than mild diabetic retinopathy: rescreen in 12 months.” The device provides a screening decision without needing a clinician to interpret the image or results, which makes it usable by healthcare providers who may not normally be involved in eye care.7

The FDA’s New Approach to Data Management

The digitization of processes, pervasive use of mobile technologies, and advancements in data gathering and analytics have created new types and more uses for data. Therefore, a central part of the FDA’s TMAP blueprint is making better use of data. This motivated the agency to create the Data Modernization Action Plan (DMAP). Specifically, this data strategy focuses on the stewardship, security, quality control, analysis, and use of data in developing state-of-the-art products and solutions. At a high level, the DMAP will enable the agency to align technology and innovation across multiple industries, enhance its data practices, and foster efficient collaboration across its growing, diverse workforce.8

Foundational requirements for the FDA’s modernized technology infrastructure include virtual data storage, problem-specific software, and solutions for efficiently exchanging data. This calls for the robust computing power and extensive data storage capacity of a cloud environment. Overall, the plan will support broader efficiencies in communication and data sharing.

The Necessity of the Data Management Strategy

Understanding the potential of what can be achieved through modernization and big data is largely the impetus for the FDA’s DMAP strategy. Evolving technologies are ushering in next-generation solutions and more precise processes. Data is highly integral to this progress. For example, the ability to closely track and trace medical and food products can increase responsiveness to emergencies and unplanned events such as pandemics, natural disasters, and global supply chain disruptions. Capturing more real-world data also boosts the effectiveness and diversity of clinical trials.

Delivering Measurable Value with Modernization

Throughout 2020, the FDA labored to expedite the drug approval process to find treatments for COVID-19. During this time, the agency has also been making notable progress with its technology modernization action plans. The ability to use data from many new sources has led to better understanding how healthcare-related solutions are performing. For example, experts can now better pinpoint the source of a foodborne illness, which is an emerging public health threat. AI can rapidly analyze data and automatically identify connections and patterns in the data that people or rules-based screening systems can easily overlook. The agency has been leveraging AI to expand its predictive analytics capabilities in a pilot program for screening imported foods as part of its food safety initiative.

The pilot program focuses on imported seafood due to the high percentage of the product that is imported versus that produced domestically. This concentration allows for targeted learning and controlled experimentation within the import screening process. This approach has real potential to be a tool that expedites the clearance of lower risk seafood shipments and identifies those that are higher risk. The proof of concept [demonstrates] that AI/ML could almost triple the likelihood that we will identify a shipment containing products of public health concern.9

Modernization Going Forward

Overall, the TMAP and DMAP have enabled the FDA to take a more advanced, proactive approach to pursuing its regulatory mission of enhancing public health. The modernization strategies are a work in progress, but the agency has identified specific metrics to measure the success of each strategy. These include a recognizable reduction in regulatory cycles, broader data sharing across the organization and the life sciences industry, and the creation of a culture of empowerment among its workforce. The agency plans to build on the experience of its modernization approaches, continue planning for resource needs, and continue with launching the individual programs outlined in both the TMAP and DMAP initiatives.


  1. “Number of Original Investigational New Drug (IND) applications received in the quarter,” U.S. Food and Drug Administration, Dec. 31, 2020.
  2. Can Nanotechnology Deliver Big Drug Benefits?” BioPharm, June 1, 2019.
  3. FDA’s Technology Modernization Action Plan,” U.S. Food and Drug Administration, Sep. 17, 2019.
  4. FDA’s Data Modernization Action Plan: Putting Data to Work for Public Health,” Janet Woodcock and Amy Abernethy, U.S. Food and Drug Administration, Mar. 3, 2021.
  5. Artificial Intelligence and Machine Learning in Software as a Medical Device,” Artificial Intelligence and Machine Learning (AI/ML) Software as a Medical Device Action Plan, U.S. Food and Drug Administration (FDA), Jan. 12, 2021.
  6. Artificial Intelligence and Personalized Medicine,” Nicholas J. Schork, National Center for Biotechnology Information, Oct. 22, 2020.
  7. AI Device for Detecting Diabetic Retinopathy Earns Swift FDA Approval,” Keng Jin Lee, American Academy of Ophthalmology, April 12, 2018.
  8. Data Modernization Action Plan: A Framework for FDA’s Data Strategy,” U.S. Food and Drug Administration, Nov. 10, 2020.
  9. Import Screening Pilot Unleashes the Power of Data and Leverages Artificial Intelligence,” Stephen M. Hahn, U.S. Food and Drug Administration, Aug. 31, 2020.

This article was written by Sue Marchant, Senior Director of Product for Data and AI, MasterControl, Salt Lake City, UT. For more information, click here .