GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Adverse Childhood Experiences (ACEs) Literature Review Dashboard
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 solution will impact the process of conducting literature reviews for scientific research related to ACEs.

Identifying relevant articles in a literature review is a time intensive process. Utilizing large language models, this system enables quick identification of relevant articles to be included in a ACEs literature database. This database allows ACEs researchers within CDC's National Center for Injury Prevention and Control a quick reference to develop hypotheses and identify gaps in the ACEs literature
12_outputs: This solution automatically determines the relevancy of published journal articles. Relevant articles are included in a master dataset that is linked to a PowerBI
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 2/1/2024
19_date_acq_dev_began: 3/1/2024
20_date_implemented:  
21_date_retired:  
22_dev_method: Developed with contracting resources.
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: Data used are published journal articles which may or may not be owned by CDC but may be accessed either through freely accessible sources or specific agreements CDC has to research articles.
34_data_docs: Documentation is missing or not 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: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
2_use_case_name: Detecting Stimulant and Opioid Misuse and Illicit Use
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: Federal Health Survey analysis. The machine learning models created in this project will have an impact on the statistical analysis of electronic health record (EHR) data gathered through the (NHCS) National Hospital Care Survey. Furthermore, when the model is made available to the public, it may also influence the analysis of other user datasets that contain EHR clinical notes.

Certain non-therapeutic models do not have corresponding medical codes available in clinical notes and the information is only available in free-text fields. The machine learning models help to discover non-therapeutic drug use as described in EHR clinical notes which are novel insights previously impossible to gleam from EHRs without AI.
12_outputs: Two machine learning models will be created, one for internal use and another for public release. In both instances, these models will be utilized alongside rule-based text analysis to ascertain whether a patient in a hospital setting has used a drug therapeutically (in accordance with medical prescription) or non-therapeutically. This information is used to gather new insights from EHRs for health statistics published through the NHCS.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 3/1/2024
19_date_acq_dev_began: 9/1/2024
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: National Hospital Care Survey 2020 clinical notes
34_data_docs: Documentation has been partially completed
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato: No
41_system_name:  
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: No
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.
2_use_case_name: DHP Virtual Assistant
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: Literature Review for HIV research. The AI virtual assistant will help HIV researchers with information retrieval on information related to HIV and HIV research. 

Our AI virtual assistant will help increase productivity among our HIV researchers by retrieving information related to HIV and HIV research.
12_outputs: This AI assistant will use retrieval augmented generation (RAG) to return information related to HIV based on a user's query and other documents.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 11/1/2024
19_date_acq_dev_began: 11/1/2024
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: Yes
33_agency_data: Internal documentation containing mapped eHARS LOINC codes, IQVIA data dictionaries, APRs
34_data_docs: Documentation is complete
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: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
2_use_case_name: Fuzzy matching tool
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: Clearance and review management of CDC publication. CDC's Office of Science (OS) manages the eClearance information system, which is responsible for tracking the submission of CDC-authored journal manuscripts and Morbidity and Mortality Weekly Reports (MMWRs) for CDC's internal peer review process.

This tool will help ensure CDC-authored publications have completed the National Institutes of Health Manuscript Submission (NIHMS) process and are compliant with CDC's public access to publications policy. It will also be used to help centers and divisions identify publications from their organization for science prioritization and impact analyses.
12_outputs: The fuzzy matching tool uses pre-trained, open source LLMs to identify similar records between eClearance submissions and CDC-authored publications. This tool is intended for internal use only.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 9/1/2022
19_date_acq_dev_began: 3/1/2023
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: eClearance submissions data and Science Clips data
34_data_docs: Documentation is complete
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato: No
41_system_name:  
42_dev_tools_wait: Less than 6 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.
2_use_case_name: Global (GIB) Initiative for Influenza Vaccine Equity (GIIVE): Ensuring Universal Access
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: Literature Review

The output is a list of abstracts that may have related information to vaccine inequity. These identified abstracts will be reviewed "by-hand" and then those associated publications will be reviewed by epidemiologists further.
12_outputs: The Through APIs, large language models are used to review a large number of abstracts to help identify which kinds of groups may be least likely to obtain vaccines in the global space--marginalized groups within country, for example.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2024
19_date_acq_dev_began: 10/1/2024
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: Data used are published journal articles which may or may not be owned by CDC but may be accessed either through freely accessible sources or specific agreements CDC has to research articles.
34_data_docs: Documentation has been partially completed
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: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for 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.
2_use_case_name: HIV Data Virtual Assistant
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: The AI will impact the data retrieval and automation process for HIV researchers, specifically in providing information from datasets and potentially generating code for desired variable names and analysis or generating information about a particular dataset.

This solution aims to alleviate the challenges faced by scientists searching for the appropriate HIV-related data to address their inquiries. This will improve productivity and research efforts.
12_outputs: This AI assistant will use retrieval augmented generation (RAG) to return information related to HIV based on a user's query. Our aim is to input all dataset documentation and metadata into an index with the goal of creating a virtual assistant. This assistant offers support to our HIV researchers by providing the information they seek from the data and potentially generating SAS, R, or Python code for desired variable names. For instance, if we require a dataset containing HIV PrEP information, we can request it to generate a list of current datasets that contain data on PrEP medication usage in 2019, along with the associated variable names. Subsequently, we can instruct it to produce lines of code, such as a logistic regression, which can be readily copied and pasted into our analysis software of choice. Another example would be if our researchers would like information returned about a given dataset, this assistant could return specific information based on the researcher's query.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 11/1/2024
19_date_acq_dev_began: 11/1/2024
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: Yes
33_agency_data: Internal documentation containing mapped eHARS LOINC codes, IQVIA data dictionaries
34_data_docs: Documentation is complete
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: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
2_use_case_name: Identify infrastructure supports for physical activity (e.g. sidewalks) in satellite and roadway images
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: The physical environment, such as sidewalk availability, in areas may be correlated to levels of physical activity. The Physical Activity (PA) and Health Branch is responsible for monitoring and gathering data on physical activity behaviors, as well as assessing the environments that promote physical activity.

The implementation of this technology has the potential to streamline the surveillance of sidewalks, which play a crucial role in supporting physical activity (PA). The existing methods for cataloging sidewalks, such as manual inspection, are labor-intensive and costly. However, this technology has the capability to significantly minimize the effort required for such tasks.
12_outputs: The Division of Nutrition, Physical Activity, and Obesity within CDC's National Center for Chronic Disease Prevention and Health Promotion aims to explore and advance the use of machine learning methods for identifying sidewalks, bicycle lanes, and other relevant infrastructure in various types of images, including satellite and roadway images. The input data would consist of image-based data, while the outputs could take the form of geocoded data tables, maps, GIS layers, or summary reports. The goal is to develop and promote these techniques to enhance understanding and analysis of built environments related to physical activity.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 9/1/2022
19_date_acq_dev_began: 9/1/2023
20_date_implemented:  
21_date_retired:  
22_dev_method: Developed with both contracting and in-house resources.
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: none - we used publicly-available images
34_data_docs: Documentation is complete
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato: No
41_system_name:  
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: No
49_existing_reuse: Re-use production level code from a different use-case: This use case re-uses production level code and/or internally developed code libraries for model development from existing AI systems or repositories inside the agency.
50_internal_review: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
2_use_case_name: LaserAI
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CDC
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: This project aims to evaluate a commercial off the shelf, software as a service systematic review project management software. This software uses machine learning/ natural language processing to support title and abstract screening, PDF retrieval, full text review, and most importantly data extraction to reduce screening time and improve accuracy of all phases of a systematic review.

The extracted data will be synthesized and graded, and will be used to inform the development of evidence-based infection prevention and control recommendations for healthcare settings.
12_outputs: This AI uses natural language process to (1) identify potentially relevant articles and prioritize them for screening, (2) retrieve PDFs from PubMed, and (3) suggest data in PDFs for extraction.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 4/1/2024
19_date_acq_dev_began: 4/1/2024
20_date_implemented:  
21_date_retired:  
22_dev_method: Developed with contracting resources.
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: None: Any data used to train the AI is publicly available, peer-reviewed data.
34_data_docs: Documentation is missing or not available
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato: No
41_system_name:  
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Re-use production level code and/or data products: This use case re-uses production level code as well as data products that have been published or made widely available for data science teams for within the agency.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
2_use_case_name: NewsScape
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: Development of early warning indicators. News articles can form an early warning indicator of public health events and other pieces of information which can be utilized across all major domains of public health. Because of the quantity of news articles, manual efforts are impossible to gather this information.

There are a variety of endpoints that this AI output could help support. Various teams across the CDC have expressed interest in being able to quickly get the right news content, with summaries of those articles to help support outbreak detection, report generation, surveillance and monitoring of pathogen specific news, etc. AI lets us efficiently filter and summarize thousands of news articles a day into a handful of daily "news events" that user can glean information from. 
12_outputs: NewsScape is a AI enabled news aggregation and summarization that is hosted within the 1CDP platform. The main motivation to build  NewsScape was to develop a system that uses Large Language Models (LLMs) to surface relevant insight from recent news articles. NewsScape ingests a high volume of news articles, in the order of thousands of news articles everyday, and surfaces the information from them that related to the topic we were interested (for example, pathogen related news articles, or U.S. medical supply chain updates).  The tool can be customized based on specific program office needs, and can be deployed independently of one another so that one program office can have their own custom version of NewsScape installed.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 1/1/2023
19_date_acq_dev_began: 1/1/2023
20_date_implemented:  
21_date_retired:  
22_dev_method: Developed with both contracting and in-house resources.
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
34_data_docs: Documentation has been partially completed
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato: Yes
41_system_name: 1 CDC Data Platform (1CDP)
42_dev_tools_wait: 6-12 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: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
2_use_case_name: RAPID Analysis of Policy and Program Documents (RAPID)
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: Policy and documentation awareness and evaluation

The web application will: 1) save staff time by streamlining laborious document reviews; 2) expand capacity by reducing the need for specialized staff training and skills; 3) provide complete, plain language answers to policy surveillance and evaluation questions along with binary codes or scores; 4) answer questions consistently (i.e., reduce intra-rater variability for redundant coding); and 5) enable easy validation of AI-provided answers.
12_outputs: An internal web application  will enable CDC users to: 1) import and securely store policy or program documents; 2) search for relevant documents; 3) ask questions of relevant text segments; 4) validate answers; and 5) collaborate on policy surveillance and evaluation projects.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 11/1/2021
19_date_acq_dev_began: 9/1/2022
20_date_implemented:  
21_date_retired:  
22_dev_method: Developed with contracting resources.
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: RAPID analysis of DNPAO policy and program data using GPT will results in project specific databases. To ensure accuracy and reliability of these AI-generated data, they will be compared to data of CDC manual reviews of the same documents by SMEs.  
34_data_docs: Documentation is missing or not 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: Re-use production level code from a different use-case: This use case re-uses production level code and/or internally developed code libraries for model development from existing AI systems or repositories inside 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.