GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Plan Certification Recommendation Model
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: Identify trends/patterns within plan certification justification templates, containing free-form text data through use of NLP and classification techniques. 

Improve efficiency of the plan certification review process
12_outputs:  build a supervised machine learning model using historical justification data and associated plan certification outcomes that can be recommended to CMS to build towards a more efficient review process.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/1/2024
19_date_acq_dev_began: 1/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: Not Reported
24_hisp_support: No
31_data_catalog: No
33_agency_data: CMS
34_data_docs: Documentation is widely available
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: AI-Assisted Data Entry
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: HHS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Inputting large amounts of data from paper forms into a digital system using AI.
11_purpose_benefits: The intended purpose is to leverage AI technology to intake paper forms and assist in the data entry process, which will increase efficiency and reducing human error.
12_outputs: The output is a digital data set converted from physical forms with the assistance of AI tool.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Transcription in Zoom and Teams
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: HHS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Transcribing and summarizing a recorded meeting or interview using AI.
11_purpose_benefits: Meetings scheduled via Zoom or Teams can be transcribed live or from recorded meetings, which improves communication. 
12_outputs: The output is a transcribed text from a live meeting or recorded meetings.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Email Prioritization in Outlook
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: HHS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Prioritizing and categorizing incoming emails using AI.
11_purpose_benefits: The Outlook in Office365 suite can categorize incoming emails based on users' definitions, which can help users manage their inbox more efficiently. The output is categorized emails.
12_outputs: The output is categorized emails.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: AI-assisted Pattern Detection
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: HHS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Identifying unusual patterns in system logs from a single incident report using AI.
11_purpose_benefits: AI technologies have been integrated into many existing log systems to identify unusual patterns, which can help detect system log issues and raise alerts for human intervention. 
12_outputs: The output is alerts and reports that highlight unusual patterns in system logs.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Report Summarization by commercial GenAI platform
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: HHS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: Some GenAI platforms, such as ChatGPT and Google Gemini, can summarize documents with non-sensitive information. 
12_outputs: The output is a summary of provided paragraphs or documents.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Information Search from Google
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: HHS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Google's search engine provides AI-assisted techniques to process search requests efficiently. 
12_outputs: The output is relevant search results.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Document Digitization by AdobePro
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: HHS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Digitizing text from scanned documents or smart forms for archival purposes using AI.
11_purpose_benefits: Adobe Pro can convert scanned documents into digital data.
12_outputs:  The output is digital data with searchable functionality.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Autocorrect in MS Word
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: HHS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: MS Word's autocorrect feature uses AI to suggest corrections, which improves the quality of written communications. 
12_outputs: The output is autocorrected words or sentences in written documents.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: AI-Assisted Real-time Collaboration
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: HHS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Collaborating in real-time using AI-assisted tools in word processors.
11_purpose_benefits: The AI-assisted real-time collaboration tool can process the inputs from team members and provide real-times stats using NLP, which offers real-time feedback for collaboration. 
12_outputs: The output includes summarization and statistical data derived from the inputs of multiple users.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Presentation Design by MS PowerPoint
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: HHS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Creating visually appealing presentations using AI-driven design suggestions.
11_purpose_benefits: MS PowerPoint can provide more appealing design suggestions based on the content. 
12_outputs: The output is multiple visual design suggestions for the user to select.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Data modeling and visualization by MS Excel
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: HHS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: MS Excel contains many data analysis toolkits to provide AI-powered data analysis and visualization, which can improve understanding and presentation of data. 
12_outputs: The outputs from these features are machine learning models and statistical analyses of given data, along with their visual representations.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: News from commercial publisher such as Google News
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: HHS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Curating news articles and updates based on user preferences using AI.
11_purpose_benefits: Major news publishers, such as Google News, send selective news based on users' preferences and recent visits. 
12_outputs: The outputs from these news providers are more customized news feeds.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Travel Route Optimization by Google Maps and Apple Maps
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: HHS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Planning travel routes using AI-driven map applications.
11_purpose_benefits: Major commercial map service providers, such as Google Maps or Apple Maps, can automate and optimize travel routes based on algorithms and live traffic data, which can save time and provide optimal routes. 
12_outputs: The output from these map services is an optimized travel route based on the user's preferences and live traffic data.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: AI-Powered Travel Booking
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: HHS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Finding and booking travel accommodations using AI-powered platforms.
11_purpose_benefits: Major travel booking websites, such as hotels.com, can provide booking options based on customers' preferences. Algorithms running in such services can optimize travel accommodations, which can save time and provide better deals. 
12_outputs: The output is a list of travel accommodations for customers to choose from and make booking decisions.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Facial Recognition Unlock
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: HHS
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Unlocking smartphones or other devices without the need for passwords or PINs using AI-based facial recognition.
11_purpose_benefits: The Face ID feature in iPhones takes advantage of AI technology to securely unlock the phone. 
12_outputs: This feature allows the phone to be unlocked using facial recognition technology.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: AI to Improve Public Access to the Administrative Appeals Process
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: DAB
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The DAB adjudicates  disputes under more than 60 statutory authorities.  The workload is divided between the DAB's four adjudicatory divisions (e.g., the Board, the Civil Remedies Division, the Medicare Operations Division and the Alternative Disputes Resolution Division. Helping appellants decide which entity within the DAB is responsible for adjudicating their appeal will increase efficiency in adjudication and customer experience. 

The DAB's appellant community (e.g., e-file users) will be provided immediate assistance and accurate information; deaf and hard-of-hearing populations and individuals with limited English proficiency are helped without the use of intermediaries, such as interpreters, translators, and relay services. After determining the appropriate division within the DAB responsible for adjudicating the appeal, the Chatbot can provide a link to the e-file website directly connecting the user to the correct e-file system.  Other benefits include: Improve public access to the appeals process by providing instant, accurate and complete filing information; Increase the DAB's productivity by refocusing the workforce to case resolution instead of responding the filing inquires and processing and appropriately routing misfiled appeals; and Improve resource allocation based on each Division's actual workload.  
12_outputs: A chatbot on the DAB's website can direct filers to the correct Division and e-file system with which the appeal should be filed by using natural language processing (e.g., questions and responses) with the ability to further refine and increase accuracy based on user response. 
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 4/1/2023
19_date_acq_dev_began: 7/1/2023
22_dev_method: Developed in-house.
31_data_catalog: Yes
34_data_docs: Documentation is complete
40_has_ato: Yes
41_system_name: DAB E-file
42_dev_tools_wait: More than 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: 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: AI Use Policy Tool
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: DAB
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The DAB is responsible for issuing fair, impartial, legally correct and defensible determinations which serve as the final decision of the DHHS Secretary.  The AI Use Policy is designed to promote public confidence in the integrity of DAB decisions.  

More effective quality review tool and faster identification of data trends that require additional analysis.  
12_outputs: AI Use Policy Tool will scan DAB decisions to ensure compliance with quality review standards (e.g., protect PHI, PII and FTI) and identify work product that potentially violates the prohibition of the use of generative AI in DAB decisions.  
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 4/1/2023
19_date_acq_dev_began: 9/1/2023
22_dev_method: Developed in-house.
31_data_catalog: Yes
33_agency_data: TBD
34_data_docs: Documentation is complete
40_has_ato: Yes
41_system_name: DABACTS
42_dev_tools_wait: More than 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: 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: Resources to Assist the Advisory Board In Identifying AI Tools for Use In An Adjudication Environment
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: DAB
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The adjudication process from docket analysis, case processing, issuing a decision and quality review (after adjudication) relies on the analysis of large quantities of data. 

AI or machine learning tools will help the Advisory Board determine which technology will best serve the Agency's needs in an adjudication environment. 
12_outputs: The use of AI to analyze the voluminous data received from appellants and interested parties to identify trends and increase efficiency in the adjudication process.  
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 4/1/2023
19_date_acq_dev_began: 9/1/2023
22_dev_method: Developed in-house.
31_data_catalog: No
34_data_docs: Documentation is complete
40_has_ato: No
42_dev_tools_wait: More than 12 months
43_infra_provisioned: No
45_compute_request: No
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: 356H ML Facility Supply Chain Role Classification
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: FDA
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Applicants submit unstructured text to indicate a facility’s supply chain role, which range from manufacturing, testing, storing, packaging, etc. This use case extracts the supply chain role based on the text and presents it to data stewards who perform data operations

Improve the efficiency of evaluating 356H forms to assess a facility's supply chain role, thereby decreasing the time required for the processing of submissions.
12_outputs: Extracts a facility's supply chain role from industry submissions and displays identified roles on a data-stewards screen for human verification and final determination.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2022
19_date_acq_dev_began: 6/1/2022
20_date_implemented: 5/1/2023
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: Not Reported
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Applicant submissions
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: CDEROne Analytics: FISMA-high environment
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: AI Tool for Risk-based FAR Review & Decision Support
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: FDA
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Assist Field Alert Report (FAR) reviewers by providing risk-based intelligence and insights for review and decision support with a Human-in-the-Loop.

Assist with streamlining an efficient FAR review process by summarizing risk profiles for human exploration that is evaluated with additional data to assist human decisioning.
12_outputs: AI-based machine learning classification of FAR risk into low, medium, and high; provides insights on problem clusters, rare-events, and source variables.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2020
19_date_acq_dev_began: 10/1/2020
20_date_implemented: 10/1/2024
22_dev_method: Developed in-house.
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Internal Field Alert Reports data from LSMV
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: CDEROne Analytics: FISMA-high environment
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: No
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: Analytics-Driven Supplement Evaluation (ASE)
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: FDA
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: This AI use case supports the triage and staff assignment process for the review of post-market Change Being Effected (CBE) supplement submissions.

The output of the AI component contributes to the triage and staff assignment process, and summarizes submitted information to support the decision of the reviewers of the CBE 30/0 supplemental submissions.
12_outputs: Using a Convolutional Neural Network (NN) model, in combination with a rules-based approach, produces an output that helps staff triage CBE submission review
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/1/2022
19_date_acq_dev_began: 10/1/2022
22_dev_method: Developed in-house.
31_data_catalog: No
33_agency_data: Data submitted in applicants' supplemental submissions
34_data_docs: Documentation is complete
40_has_ato: No
42_dev_tools_wait: 6-12 months
43_infra_provisioned: No
45_compute_request: No
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: Augmenting death and cause of death ascertainment in observational data sources
3_agency: Department of Health and Human Services
3_abr: HHS
4_bureau: FDA
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: Augment assessment of mortality in electronic health records to support investigations to develop probabilistic estimates for causes of death in the Sentinel system.

The extraction of mortality information improves studies in Sentinel using mortality as an endpoint by enhancing on the quality of mortality data.
12_outputs: Augment assessment of mortality (date of death, cause of death) by applying Natural Language Processing (NLP) on healthcare narrative text and administrative codes.
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: 12/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 75F40119D10037 - 7540119F19002
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: `- National Death Index (NDI) data
- State Death Certificate Registery
34_data_docs: Documentation has been partially completed
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Sentinel
42_dev_tools_wait: 6-12 months
43_infra_provisioned: No
45_compute_request: No
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.