Webinar: Improving Public Comment Review with Automation, Machine Learning, and Advanced Analytics—RTI SmartReview
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Each year, Federal agencies are required to obtain input on potential policy changes during a public comment review period. Timely identification of relevant comments is critical to maximize time for the government to review, consider, and respond to comments. How do you identify relevant comments in a timely manner to inform critical policy changes?
RTI resolved this issue by developing a data science tool and workflow—RTI SmartReview—that enables Federal agencies to quickly prioritize, review, and respond to public comments. RTI SmartReview uses an automated data pipeline, natural language processing, and machine learning to select relevant comments and integrate organized comment data into dashboards quickly, accurately, and automatically.
View the Slides
During the SmartReview webinar, attendees learned about:
- Applying advanced analytical methods to solve rulemaking problems and automate routine manual tasks.
- How RTI SmartReview assessed more than 30,000 public comments for the Medicare Shared Savings Program and identified relevant comments for consideration within two days of the comment period closing.
- The importance of human-centered design in effectively solving problems and identifying successful methods.
- The benefits of software applications like RTI SmartReview for public comment review.
Meet Our Presenters
Kim Danforth
Senior Research Public Health Analyst, Health Care Quality and Outcomes Center
Kim Danforth is an epidemiologist and health services researcher whose work focuses on cancer and outpatient safety. Dr. Danforth recently helped develop an algorithm using coded data and natural language processing to identify lung nodules. The algorithm has been used to facilitate multiple studies where identifying cancerous lung nodules was difficult. Her current work includes filling a critical gap in cancer registry data by extracting bladder cancer recurrence and progression from free-text notes.
Rob Chew
Senior Research Data Scientist, Center for Data Science
Rob Chew helps subject matter experts solve complex data problems by using his expertise in machine learning, data visualization, software development, and computational social science. Dedicated to interdisciplinary research, he has successfully integrated data science into Federal clients’ projects spanning health care, criminal justice, public health, and the environment.
Anna Godwin
Research Data Scientist, Center for Data Science
Anna Godwin is a research data scientist in the Center for Data Science, a Center of Excellence within RTI dedicated to practicing data science for social good, deploying data science solutions into all RTI businesses. Anna collaborates to develop data science solutions with the end-user in mind. She has experience in data analysis, model development, data visualization, and model deployments.
Ian Thomas
Research Data Scientist, Center for Data Science
Ian Thomas works on large-scale data processing and interactive data visualizations in the Center for Data Science. With 15 years of professional experience in Internet technologies, he has worked as a data engineer and reporting analyst where he developed large-scale data pipelines for collecting and analyzing incoming data from millions of users, using the information to build interactive dashboards.
