2_use_case_name: Automation of Receipt & Referral Process of NIDDK grant applications
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: NIH
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: This use case is centrally reported under OER's Internal Referral Module. NIDDK receives about 100 to 500 applications per week. NIDDK referral staff uses the IMPAC II QVR tool to stratify the applications between divisions based on a “Like Matrix” function to match the Division’s portfolios and the Title, Abstract, and specific aims of grant applications. This process is laborious and manual. Hence, automation was proposed. The scope of the effort is to provide a capability for NIDDK to analyze grant applications using NIDDK-specific business rules, complete the pilot, and the full implementation of the Artificial Intelligence (AI) and Natural Language Processing (NLP) tools for auto assignment of program class codes.

NIDDK receives about 100 to 500 applications per week. The AI and machine learning tool will stratify the applications between programmatic divisions and Program Class Code (PCC) to match the Division’s portfolios based on the Title, Abstract, and specific aims of grant applications. It collectively saves NIDDK Program and Referral Staff 3000+ hours annually on Receipt and Referral activities. In addition, it instantly recommends PCC, allows Division designees to accept or change recommendations, tracks all grants that need PCC assignment, gives real-time information about the application status, and support business rules/generates reports.
12_outputs: Input: Title, Abstract, Public Health Narrative, Specific Aims of grant applications from the FY 2018 to 2022.

Output: Division and Program Class Code (PCC) of grants from FY 23/24.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/1/2021
19_date_acq_dev_began: 10/1/2021
20_date_implemented: 10/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 2-DK22-560-02 and OER-23-014
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Yes, It used the NIH/NIDDK grants applications data submitted by research investigators. The prediction models use the Title, Abstract, Public Health Narrative, and specific aims of grant applications from the FY 2018 to 2022 to train the models and FY 23 and F24 grants applications data were used for predicting programmatic divisions and Program Class Codes (PCC).
34_data_docs: Documentation is complete
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
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.