- The Food and Drug Administration has rolled out a plan to modernize how the agency handles and responds to the many sources of data it relies upon. Through the framework, dubbed DMAP, the FDA aims to improve how its runs data projects and build the expertise of its workforce.
- "Even small advances in our ability to gain useful insights from data can represent significant opportunities," wrote Janet Woodcock and Amy Abernethy, respectively the acting commissioner and acting chief information officer at the FDA, in a blog post.
- The FDA created a plan to modernize its technology two years ago, setting out actions the agency said would equip it to "securely receive, store, exchange, link and analyze data" at scale. With that plan now in place, the FDA has turned its attention to how it will manage and use data.
With DMAP, the FDA has set out three broad goals. First, FDA aims to "identify and execute high value driver projects for individual centers and the agency." Woodcock and Abernethy shared further details of that part of the plan in a blog post.
"Driver projects for DMAP are defined as initiatives with measurable value that help multiple stakeholders envision what is possible, allow technical and data experts to identify needed solutions, and develop foundational capabilities," they wrote. "This strategy avoids the pitfalls of focusing on data collection first and only then looking for questions the data can answer."
One focus will be on predictive models and artificial intelligence, as well as projects that address traditional performance indicators. For each project, the FDA plans to measure and communicate the value of the initiative to internal and external stakeholders.
One example is the agency's participation in the public-private COVID-19 Evidence Accelerator, which gathers real world data to guide the response to the pandemic.
The agency's second objective is to "develop consistent and repeatable data practices across the agency." Woodcock and Abernethy described that initiative as creating "foundational capabilities" in areas such as the identification, curation, governance and automation of data. In practice, that will mean assessing data practices, establishing an agency-wide governance model and piloting the creation of an "enterprise data model."
The FDA's final goal is to "create and sustain a strong talent network combining internal strengths with key external partnerships." Woodcock and Abernethy said it's critical the agency has a "strong focus on talent and elastic talent networks, to ensure that the modernization plan will be swift, consistent and economical."