FDA has shared a Data Modernization Action Plan setting out its response to the proliferation and diversification of data sources. Through DMAP, FDA aims to identify and run data projects with measurable value, develop repeatable practices and build a talent network.
FDA is working toward those goals in the belief that “even small advances in our ability to gain useful insights from data can represent significant opportunities,” Janet Woodcock and Amy Abernethy, respectively the acting commissioner and acting chief information officer at FDA wrote in a blog post.
In 2019, FDA created the Technology Modernization Action Plan. The plan set out the near-term actions the agency proposed to take to equip itself to “securely receive, store, exchange, link and analyze data” at scale. With the infrastructure plan in place, FDA turned its attention to how it will manage and use data.
The result is DMAP, a plan with three broad goals. First, FDA wants 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 the 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. This strategy avoids the pitfalls of focusing on data collection first and only then looking for questions the data can answer,” the FDA officials wrote.
One key focus will be use of predictive models and artificial intelligence, as well as projects that address traditional performance indicators. For each project, FDA plans to measure and communicate the value of the initiative to internal and external stakeholders.
An example FDA gave 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 second overarching 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 key data practices, establishing an agencywide governance model and piloting the creation of an Enterprise Data Model.
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 is critical that FDA has a “strong focus on talent and elastic talent networks, to ensure that the modernization plan will be swift, consistent and economical.”
Steps toward the realization of that goal include identification of skills FDA needs to conduct data projects, development of strategies for recruiting and retaining talent, as well as the creation of an internal network for sharing knowledge and resources.