Using Databricks Genie for Routine Immunization Data Insights
Description
The volume of immunization data reported quarterly is approximately 5 billion records. Analyzing this data to gather high-level insights requires complex coding and manipulation. By using a Large Language Model (LLM) based approach, the solution offers a plain language query capability that generates code to provide high-level insights without the need to move or create specific views for program needs. This approach is cost-effective, timely, and provides insights that can be used to improve data quality and inform data management planning by programs.
Detailed example
SQL Code, Reports, Visualization and Charts
AI / analytics pattern
Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
The use of a Large Language Model (LLM) based approach for generating code to analyze immunization data offers several promising benefits for the CDC's immunization program staff and data operation team: 1. Enhanced Efficiency and Time Savings: By enabling plain language queries, this approach significantly reduces the time and effort required for complex data manipulation and coding. This will allow program and data ops staff to focus on more critical tasks, potentially plan the data management tasks efficiently. 2. Improved Data Quality and Management: The insights generated can help identify data quality issues and inform better data management practices. This will lead to potentially more accurate and reliable data. 3. Cost-Effectiveness: Simplifying the analysis process reduces the need for extensive manual labor and specialized coding skills and compute costs. 4. Scalability: Handling approximately 5 billion records quarterly, this approach can scale to meet the demands of large datasets, ensuring timely and comprehensive analysis.
Controls / human review
ATO: Yes; PIA: Not published