Using Databricks Genie for Routine Immunization Data Insights

Overview

Development stage

PilotSource: b) Pilot – The use case has been deployed in a limited test or pilot capacity.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

2025-06 (month precision)Source: Jun-25

Withheld from public reporting

NoSource: a) No

Mission

Topic area

Science

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

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.

System outputs

SQL Code, Reports, Visualization and Charts

Technology

AI classification

Generative AISource: Generative AI: AI that generates new or synthetic content (e.g., images, videos, audio, text, code).

System name(s)

EDAV (Enterprise Data Analytics and Visualization Platform)

Custom-developed code

No

Public source code

Not reported

Sourcing

How it was built

Contract and in-houseSource: c) Developed with both contracting and in-house resources

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

No

Privacy Impact Assessment

Not reported

Authorization to Operate

Yes

Demographic features

k) None of the above

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported