2_use_case_name: Automated Analysis of Injury Control Research Center (ICRC) Annual Progress Reports (APRs) using Large Language Models
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: This AI affects the process of reviewing and analyzing the Annual Progress Reports (APRs) submitted by Injury Control Research Centers (ICRCs). Specifically, the AI will focus on identifying and analyzing text sections of the APRs that detail reported challenges, as well as other text-heavy sections in future stages of the project. The AI is designed to streamline the review process, improve efficiency, and support the evaluation of the performance and progress of ICRC-funded activities.

The output of the AI will help to quickly and efficiently identify key challenges and insights from ICRC APRs, enabling more effective decision-making in the review process. By automating the extraction and analysis of critical information, the AI allows the ICRC team to focus on higher-level evaluation and strategic planning. The AI will help reduce the time and resources needed for manual review, improve the consistency and accuracy of assessments, and facilitate faster responses to ICRC needs. Ultimately, this will support ICRCs in overcoming challenges and achieving their research and injury control goals, benefiting the public health system as a whole.
12_outputs: The AI analyzes the textual content of APRs. We have started our analysis by focusing on the sections detailing the challenges faced by ICRCs during the 2019 funding cycle. The AI identifies key themes, trends, and critical information that may require further attention. The AI methodology extracts insights and patterns from the data, which can then be compared with manual qualitative analysis outcomes. In subsequent stages, the AI will be expanded to analyze other sections of the APRs, such as progress toward goals and program impact.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 8/1/2023
19_date_acq_dev_began: 12/1/2023
20_date_implemented:  
21_date_retired:  
22_dev_method: Developed in-house.
23_contract_piids:  
24_hisp_support:  
25_hisp_name:  
26_public_service:  
27_public_info:  
28_iqa_compliance:  
29_contains_pii:  
30_saop_review:  
31_data_catalog: No
33_agency_data: Injury Control Research Center Annual Progress Reports
34_data_docs: Documentation is widely available
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato: Yes
41_system_name: Enterprise Data Analytics and Visualization (EDAV) Platform
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.