FedHealthIT’s President, Susan Sharer, recently had the opportunity to sit down with Booz Allen Hamilton Principal, Lauren Neal, recognized at the recent Women Leading for Impact in Federal Technology awards, to discuss modernization, challenges to implementation, women leaders, and the future.
The Process and Journey
I’ve worked with FDA for over 10 years and have worked to help the Agency modernize, beginning the journey of getting from understanding data, to building out platforms and understanding challenges to address. Modernization is about how we improve the process by bringing together more evidence, weeding through all of the data to get to where the real information is that can be used.
Driving decisions using Natural Language Processing (NLP) or machine learning requires partnering domain expertise with technology to make Government’s life easier to support better decisions. I love when we see where applications in technology can change the way Government is operating to make a difference.
Modernization by Steps
Modernization often starts with a simple use case for which we work on demo prototype to show what is possible. Looking at how you actually understand trends, what data can help you understand how to better intervene, you can then build tools to allow users to see the outcomes and this is where you gain buy-in.
The challenge can be in creating prototypes that are actual operationalized solutions that can be incorporated into workflows. It’s important to think about that while you’re developing, to understand how it works, how it can be adapted, how it will fit into the daily workflow and how it delivers value.
Challenges to Implementing
The biggest challenge to implementation can be overcome by ensuring that whatever we develop can be handed over to a Government client so they can continue to use it and can understand it well enough to trouble shoot for themselves. That means training and education is key. We never want to do something that is great in theory or in a lab but that is hard to understand, is not transparent enough, or is not easy for our clients to use.
Part of that is, to ensure we aren’t just putting out cool stuff but instead are focused on solutions people can actually use and that takes into account the client concerns.
It’s also important to ensure clients are learning about technology so it is not foreign. Through partnerships, we offer courses focused on learning, on how to use new packages and acquiring basic NLP understanding and skills and so on.
We have seen agencies assessing where their workforce is, where there are gaps are in computational sciences and support areas. Organizations like NIH NLM are starting to assess and training everyone on basics of data science, understanding that it’s important for everyone to understand the basics regardless of the specific role they have so they can appreciate why data is being curated and why funding is important.
The Real Impact of AI on Workforces
Some tasks we do every day are repetitive, and although necessary, don’t allow us to use our higher level creativity and cognitive ability. Automating those tasks isn’t a threat to jobs, and in many cases, aren’t even core to our main work. In the end, humans bring a higher level of thinking, creativity and decision-making, allowing them to pursue higher level tasks just enables people to do their jobs in a better way.
How do You Encourage Innovation in Government?
It helps that there are directives from the White House and within agencies, new leadership roles focused on AI and so on. We are seeing from higher levels a real interest in figuring out how to modernize and use AI.
From the industry side, it goes back to proving yourself. Often that means starting with a smaller prototype. It can mean committing your own funds through an investment internally to test processes and techniques to demonstrate what can be done to be able to show the client what is possible.
What does the Future Hold?
I think we’re still in the early stages in the health space. We’re starting to make decisions using machine learning but there are still challenges that remain. Maybe five years ago there were a handful of solutions to comb through data more quickly but now clients are thinking about more complex data and trying to think of ways to ensure we can look at all modalities of data. The future will bring all types of data together and technologies like AI will help us do more.
Women in STEM
I’ve always been passionate about ensuring there are more women leaders in STEM. When we think about algorithms in AI, the perspective and experience women bring helps ensure different views are covered.
It’s important to have women in leadership roles and while there are more conferences and events with women as keynotes and more women being recognized through award programs, it isn’t enough. There are still many events where the speaker lineup is mainly men. For both Government and industry, it’s important that we continue to push forward, to make it a priority to work on this.
At Booz Allen, my goal is to ensure we have a community of women who feel they have pathways to become leaders and to be successful, to give them a supportive environment. Women are so qualified. They may not be the first to volunteer for tougher challenges, and may need encouragement to get there but once there, they knock it out. Women need to see themselves as leaders.
About Lauren Neal
Dr. Lauren Neal has more than 15 years of experience in the life sciences industry with a focus on creating and delivering data science and artificial intelligence solutions to accelerate precision medicine innovation. Lauren leads efforts to aggregate, analyze, and interpret health information, developing new processes, solutions, and information architectures that leverage a variety of data types—medical images, electronic health records, wearable data, and -omics data. She serves as a capability lead for investments and client engagements across multiple Federal health clients, including the U.S. Food and Drug Administration, the National Institutes of Health, and Military health organizations.
Lauren is the founder of the Booz Allen Women in Data Science group, which aims to develop diverse data science leaders and inspire the next generation of women data scientists. In addition, she leads efforts to develop data-driven solutions for not-for-profit organizations, and her work advancing Data Science for Social Good initiatives was recognized with a Booz Allen Excellence Award in 2017.
Lauren holds a Ph.D. in biomedical engineering from the Johns Hopkins University School of Medicine and a B.S. in electrical engineering from the University of Pennsylvania.