GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Development of in-silico genomic and patient datasets using generative ML algorithms
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 finding reference genomes

Reduce the development time of custom tools, finding reference genomes, and additional administrative burdens. The datasets developed would be beneficial in testing out bioinformatic pipelines. 
12_outputs: This solution uses NLP generative AI toolsets (e.g. ChatGPT, co-pilot, etc.) to generate code for the development of bacterial and viral in-silico genomic sequences and patient data. This will be compared to already established pipelines to determine factors such as code viability, data accuracy, code licensing, and any drawbacks and/or advantages these services provide. For example, we would ask GPT-4 a prompt, "generate python code to create Sars COV2 in-silico fastq sequencing data.". We will look at where did it reference the code base to generate the code and is it open source or have licensing requirements.  
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated:  
19_date_acq_dev_began:  
20_date_implemented:  
21_date_retired: 2/1/2024
22_dev_method:  
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:  
33_agency_data:  
34_data_docs:  
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato:  
41_system_name:  
43_infra_provisioned:  
45_compute_request:  
49_existing_reuse:  
50_internal_review:
2_use_case_name: Mastering Metadata: AI-Powered Governance for Data Insights
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 implementation of this solution will have a significant influence on the maintenance of data quality and data assets on the data.cdc.gov platform.

Accurate, reliable, and timely data available in data.cdc.gov is critical in making meaningful public health decisions. This publicly available data repository is used worldwide by researchers, policy makers, and general public to access, manage, analyze, and visualize public health data. Through this project, we will be able to scale up the magnitude and quality of datasets from NCIRD that’s made available via data.cdc.gov and thus maximizing public health impact for a diverse audience at multiple levels.
12_outputs: data.cdc.gov serves as a publicly accessible repository for CDC data, utilized by researchers, policymakers, and the general public to monitor and comprehend public health trends. Ensuring the accuracy and reliability of the data on data.cdc.gov is crucial for informed decision-making in public health. To achieve this, the proposed solution involves leveraging AI to maintain CDC's National Center for Immunization and Respiratory Diseases data assets on data.cdc.gov to automate various tasks such as checking metadata completeness, validating metadata against a schema, comparing metadata with other sources, and monitoring metadata changes over time.
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated:  
19_date_acq_dev_began:  
20_date_implemented:  
21_date_retired: 6/1/2024
22_dev_method:  
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:  
33_agency_data:  
34_data_docs:  
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato:  
41_system_name:  
43_infra_provisioned:  
45_compute_request:  
49_existing_reuse:  
50_internal_review:
2_use_case_name: PII detection using Private AI 
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: Numerous data sources contain personally identifiable information (PII) and protected health information (PHI), which cannot be publicly published or shared among federal partners due to confidentiality concerns. This includes sensitive records like death certificates, survey responses, and electronic health records. To enable broader utilization of this data and enhance insights, a PII detection system was explored in this project. Specifically, the availability of an off-the-shelf tool called Private AI was investigated for identifying PII. However, it is important to note that the license for this tool expired after one year and there are no current plans to renew the contract.

At present, the presence of PII within data and the labor intensive process to ensure PII redaction and removal has prevented broader sharing of data available within CDC. However, implementing an automated system which performs as well as humans and more quickly could significantly reduce the time required for human review, enabling faster dissemination of crucial public health information and data sources.
12_outputs: CDC's National Center for Health Statistics (NCHS) has been assessing the NLP solution provided by Private AI, which is specifically designed to detect, mask, and substitute personally identifiable information (PII) within textual data. This collection of models aims to securely identify and eliminate PII from unstructured text datasets across various platforms within the CDC network.
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated:  
19_date_acq_dev_began:  
20_date_implemented:  
21_date_retired: 3/1/2024
22_dev_method:  
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:  
33_agency_data:  
34_data_docs:  
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato:  
41_system_name:  
43_infra_provisioned:  
45_compute_request:  
49_existing_reuse:  
50_internal_review:
2_use_case_name: Reddit Post Analysis for Sexual Health Using Large Language Models
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 AI will impact the data extraction and analysis processes for understanding how users seek sexual health advice online using Reddit.

In additional to traditional public health impacts this can include reducing administrative burdens and other ancillary benefits. This is an important criteria when selecting the initial round of projects.
Understand how and why people seek sexual health advice online. This may lead to finding gaps in online health resources, identifying underserved populations who resort to using online resources, and discovering changes in sexual health behavior over time.
12_outputs: Reddit is a social media website where users can submit and respond to posts around a wide variety of topics. Reddit is organized into subcommunities called subreddits, and many people turn to sexual health focused subreddits, such as the “r/STD” subreddit, for advice. The Reddit posts can be a valuable source of text and image data since these posts reveal immediate sexual health concerns and questions that may be overlooked. However, the volume of posts makes large scale manual analysis impractical, and extracting insights using conventional methods is difficult. This solution uses large language models (LLMs) to assist in and automate data extraction and analysis to better understand how users seek sexual health advice online.
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated:  
19_date_acq_dev_began:  
20_date_implemented:  
21_date_retired: 2/1/2024
22_dev_method:  
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:  
33_agency_data:  
34_data_docs:  
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato:  
41_system_name:  
43_infra_provisioned:  
45_compute_request:  
49_existing_reuse:  
50_internal_review:
2_use_case_name: Retrieval Augmented Generation (RAG) with Q-Bank
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 searching CCQDER’s Q-Bank research publications on question evaluation for surveys. 

Q-Bank reports provide context needed to better understand and interpret data from survey estimates, as well as information about question performance and fit-for-use in various data collection contexts.
This project aims to improve access and visibility of CCQDER’s Q-Bank research publications on question evaluation for surveys. Additionally, indexing Q-Bank as part of this project should help to relieve some of the administrative burden of tagging and organizing reports.
12_outputs: This solution uses Retrieval Augmented Generation to develop an LLM-based search tool based on the Collaborating Center for Questionnaire Design and Evaluation Research’s (CCQDER) Q-Bank reports. The main motivation of the solution is to make it easier to navigate and identify reports relevant to a user’s research interests. Because the reports are already publicly available and they represent a relatively small corpus, they provide an excellent opportunity to develop prototype AI tools.
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated:  
19_date_acq_dev_began:  
20_date_implemented:  
21_date_retired: 6/1/2024
22_dev_method:  
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:  
33_agency_data:  
34_data_docs:  
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato:  
41_system_name:  
43_infra_provisioned:  
45_compute_request:  
49_existing_reuse:  
50_internal_review:
2_use_case_name: Using generative AI to gain insight of older adult falls
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 solution will impact the process of extracting information from medical narratives 

Traditional methods of data and information extraction from medical narratives involve manual scrutiny and the use of rule-based models. These methods are time-consuming and can overlook crucial details. By utilizing generative AI, a more comprehensive understanding of the circumstances surrounding injuries from falls can be achieved. Understanding the specific causes, circumstances, and outcomes of falls can lead to more targeted public health interventions.
12_outputs: This solution extracts insights regarding older adult falls injuries from Emergency Department medical narratives and testing out methods submitted. The data source to be used is the National Electronic Injury Surveillance System (NEISS) data.
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated:  
19_date_acq_dev_began:  
20_date_implemented:  
21_date_retired: 6/1/2024
22_dev_method:  
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:  
33_agency_data:  
34_data_docs:  
37_custom_code:  
38_code_access:  
39_code_link:  
40_has_ato:  
41_system_name:  
43_infra_provisioned:  
45_compute_request:  
49_existing_reuse:  
50_internal_review:
2_use_case_name: AI-assisted comment triaging tool
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Annual rulemaking-- the Physician Fee Schedule receives thousands of public comments and require assistance with reviewing and assigning the comments to the particular topics they address. 

Assists with workload for the PFS team
12_outputs: We use AI to assist in comment review, such as identifying form letters. 
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2022
19_date_acq_dev_began: 12/1/2021
20_date_implemented: 6/1/2022
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
34_data_docs: Documentation is complete
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
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: 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: Bankruptcy BOT
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The Bankruptcy/Overpayment Division of IFM is responsible for carrying out the Agency's oversight responsibilities for our contractors' debt management functions, and for protecting Medicare's interest in debt recoupment activities complicated by bankruptcy.

A timely identification of filed provider bankruptcies allows Medicare to secure recovery rights before timeframes to file a proof of claim expire.
12_outputs: The Bankruptcy BOT proactively identifies bankruptcy case with Medicare interest.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2020
19_date_acq_dev_began: 1/1/2020
20_date_implemented: 9/1/2021
22_dev_method: Developed with contracting resources.
23_contract_piids: 47QRAA18D001P
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
33_agency_data: PECOS and BCRS
34_data_docs: Documentation is missing or not available
35_demo_features: Residency Status
37_custom_code: No
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: PACER
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: 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: Central Data Abstraction Tool-Modernized (Modernized-CDAT)- Intake Process Automation (PA) Tool
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: Intake PA uses advanced capabilities (NLP, OCR, AI, ML) to automate, modernize, and reduce manual efforts related to medical record review functions within MA RADV audits

Eliminates manual review of Medical Record (MR) submissions, expedites the Intake process and realizes cost savings in both time and resources.
12_outputs: The PA-BOT tool will replace the RADV audit Intake manual functions for Steps 2 & 3 of the RADV audit process.  The RADV Intake process conducts a review of the MAO Medical Record (MR) submissions and validates that the MR submissions from MAOs have met RADV audit intake requirements and can proceed to MR abstraction/coding.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/1/2017
19_date_acq_dev_began: 11/1/2017
20_date_implemented: 4/1/2019
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: No
33_agency_data: Yes, prior RADV results were used to train the models.
34_data_docs: Documentation is widely available
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: Central Data Abstraction Tool - Modernized (CDAT-M)
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: 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: CMS Enterprise Portal Services (CMS Enterprise Portal-Chatbot)
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: CMS Enterprise Portal AI for Process Efficiency Improvement| Knowledge Management

 - Empowers users to find information or troubleshoot issues more efficiently
 - Reduces load on system help desks
 - Provides better insight into customer inquiries and issues informing process and design improvements

12_outputs:  - Automated text-based bot providing support to Portal users
 - Uses machine learning via Amazon Lex to answer a user’s question
 - Takes user input and finds the best match to the question or problem statement
 - Automates tasks such as: customer support; processing of user questions; and solving user problems/issues
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 8/1/2020
19_date_acq_dev_began: 12/1/2020
20_date_implemented: 4/1/2021
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: CMS Enterprise Portal and CMS Identity Management (IDM) user guides
34_data_docs: Documentation is widely available
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: CMS Enterprise Portal
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 published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
2_use_case_name: Enhanced Direct Enrollment Outlier Detection
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Enhanced Direct Enrollment (EDE) allows consumers to apply for and enroll in an exchange plan directly through an approved partner’s UI, without being redirected through the Healthcare.gov application. These partner systems directly interface with the APIs developed by the FFE. As EDE Partners gain more control over their application process, the FFE must ensure program integrity.

Ensure FFE EDE program integrity
12_outputs: This use-case will implement Machine Learning to identify anomalies/quality issues with partner-submitted person, application, and policy data​.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/1/2019
19_date_acq_dev_began: 1/1/2020
20_date_implemented: 1/1/2020
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: CMS
34_data_docs: Documentation is widely available
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: FFM
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.
2_use_case_name: Exchange Complaints Review Categorization  ML/AI 
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: CMS
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: The Exchange Complaints Review Contractor (ECRC) receives hundreds of consumer complaints from the Federally-facilitated Exchange (FFE) call center daily, who then must manually read each complaint and categorize each complaint into one or more categories according to the Complaint Review Standard Operating Procedure (SOP). The categorization assists CPI in determining the nature of the complaint, the action necessary, and the component within CMS who the complaint should be routed to. In general, each complaint can take up to 10 minutes to review and categorize, including a quality review component.   

The output (list of all complaints categorized) allows CMS to expediate routing the complaint to the appropriate CMS caseworker or Issuer to take the appropriate action necessary to make the consumer whole and ensure they can receive the necessary medical treatment. The output when done by AI is done in a matter of minutes, versus weeks when completed by human reviewers. 
12_outputs: CPI utilizes machine learning/artificial intelligence (AI) and continuously feeds the complaints review model thousands of complaints in order to teach the model how to accurately categorize the complaints, based on keywords. This is done in a matter of minutes instead of weeks by a human complaint reviewer.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/1/2022
19_date_acq_dev_began: 1/1/2022
20_date_implemented: 10/1/2024
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
33_agency_data: HICS, Marketplace
34_data_docs: Documentation has been partially completed
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: MIDAS
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: 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.