GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: VA Section 508 Office URL Ownership Prediction Model
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: OIT: Office of Information & Technology
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Purpose of the AI system is to predict the administrative agency that owns URLs.  The AI system reduces the level of effort and turnaround time for the OIT 508 Office to curate URLs and coordinate with VA Administrations to review and, as necessary, remediate URLs for accessibility compliance.  In the absence of the AI system both the 508 Office and VA Administrations would be required to undertake a lengthy and iterative process to identify the VA Administration that is the formal owner of a URL.
12_outputs: The AI Model has two outputs.  1) Agency Owner with responses as: VHA, VBA, NCA, VACO, OIT, OCTO, and Unknown.  2) Prediction Score ranging from 0 to 1.  The system is comprised of manually executed scripts using the AI model on local GFE.  Besides the AI model, the scripts additionally apply manually constructed business rules and create prepopulated excel files for each VA Administration to use for quarterly reviews and data entry.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 11/1/2023
19_date_acq_dev_began: 11/1/2023
20_date_implemented: 11/13/2023
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Other
31_question_type: In conjunction with the 508 Office we have confirmed that a comparable data set does not exist to with URL ownership data.
33_agency_data: The data of available URLs is obtained from Google Analytics as part of the GSA Digital Analytics Program.  Data to train the model is based on SME input from the VA administrations as part of their review process.
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
41_system_name: None.  The system is GFE operated by OIT staff member providing consultative support to 508 Office.
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: VA OIT(CRR)(DAPR)(DAPM) is the office providing support to the VA(OIT)(CRR)(RR)(508 Office).  DAPM relies exclusively upon GFE and locally installed tools in the absence of pending authorization to access AI infrastructure in the SDP.
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: Circle CVI42 
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: This software is for improving efficiency in post processing and reporting of Cardiac MRI and Cardiac CT images. 
12_outputs: It replaces manual contouring of different cardiac structures by automated segmentation. Output includes: ejection fraction, strain, stroke volume , coronary calcium score etc. All outputs of the AI based software are FULLY CHECKED  by the cardiologists for accuracy and the outputs can be easily altered by the reading physician  before they are reported in the patients chart.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 6/1/2022
19_date_acq_dev_began: 1/1/2023
20_date_implemented: 10/1/2024
40_has_ato: Yes
41_system_name: MD-LITE ID: 2278
2_use_case_name: National Cemetery Administration (NCA) Automated Claims Processing
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: NCA: National Cemetery Administration
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: Inputting large amounts of data from paper forms into a digital system using AI.
11_purpose_benefits: The purpose of the AI is to automate aspects of NCA benefits and application processing.  Currently it is applicable to the Pre-Need and Presidential Memorial Certificate programs. In the future other business lines may be included, such as the Headstones, Markers, Medallions, and Urns and Plaques programs. This benefits NCA by automating aspects of the workload (data entry, case creation, document digitization, letter creation, status updates). 
12_outputs: The Hyperscience platform (optical character recognition (OCR), natural language processing (NLP), and machine learning (ML)) extracts document data, uses application programming interfaces (APIs) to query data from other VA systems, and applies robotic process automation (RPA) to enter data into existing legacy NCA systems: Eligibility Office Automation System and Web Presidential Memorial Certificates.  As the case is processed, automation creates documents that combine relevant claimant information for use by claim agents, updates statuses, and creates letters as appropriate. No original content is created through these processes.
16_dev_stage: Operation and Maintenance
17_impact_type: Rights-Impacting

18_date_initiated: 4/1/2023
19_date_acq_dev_began: 5/1/2023
20_date_implemented: 8/14/2023
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: VA CART Adenoma Detection NLP
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits:  Extracts adenoma status for CART Veterans using Natural Language Processing (NLP) of VistA colonoscopy surgical pathology reports
12_outputs: Results contain 0 for no evidence of adenoma or 1 for evidence of adenoma in the pathology report
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/8/2024
19_date_acq_dev_began: 5/31/2024
20_date_implemented: 7/31/2024
22_dev_method: Developed in-house.
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: VistA colonoscopy surgical pathology reports
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
35_question_type: VistA colonoscopy surgical pathology reports
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: Clinical Assessment, Reporting and Tracking Application (VASI ID 1079, EMAS ID 1808) 
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: 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: Call Center Knowledge Navigator
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VBA: Veterans Benefits Administration
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: Using VA-provided procedural reference material, the tool will respond to call center agent questions with an answer and a cited reference to verify the answer. The tool is intended to speed responses by quickly identifying relevant references and summarizing procedures in the form of an answer, while also providing the agent with the means to look up the referenced material.
12_outputs: The system will output a generated response to a user's question.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 4/29/2024
19_date_acq_dev_began: 5/9/2024
20_date_implemented: 11/11/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 47QTCK18D0036
24_hisp_support: Yes
25_hisp_name: Veterans Benefits Administration
27_public_info: No
28_iqa_compliance: N/A; information is not accessible by the public.
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: The model uses data obtained from Education Service procedural resources (e.g., the School Certifying Official Handbook) to provide its responses.
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
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: Other
45_question_type: Resources through Azure have not been insufficient — implementation may show additional need.
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: National Training Team | Schools — FAQ Dashboard
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VBA: Veterans Benefits Administration
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The 1.0 dashboard clusters thousands of submitted questions during a monthly Office Hours session with School Certifying Officials and presents a "top ten" list of associated categories that have the most questions.
12_outputs: Classification and sentiment score
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 4/12/2023
19_date_acq_dev_began: 4/28/2023
20_date_implemented: 10/24/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 47QTCK18D0036
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Question-sets are obtained by exporting them from the monthly office hours sessions in Adobe Connect. These files are used to train and as the model's inputs.
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: Other
45_question_type: Additional computing resources have not been necessary for this model.
47_timely_resources: Other
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: National Training Team | Schools — FAQ Dashboard
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VBA: Veterans Benefits Administration
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The model should classify thousands of questions into categories, generate a sample question based on the category, and provide a suggested answer based on previous responses to similar questions.
12_outputs: Classification, list of questions, answers to questions
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/24/2023
19_date_acq_dev_began: 10/24/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 47QTCK18D0036
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Other
31_question_type: Data is provided via exports of questions from Adobe Connect Office Hours; answer data is provided from previous FAQ sessions conducted by the Training Staff.
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: No resources from OIT have been necessary for this project; development is undertaken by the contractor.
45_compute_request: Other
47_timely_resources: Other
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: Audit of Service Connection Designations Associated with Prescriptions
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: Reduction in waste, fraud and abuse. 
12_outputs: Binary
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/11/2011
19_date_acq_dev_began: 8/1/2011
22_dev_method: Developed in-house.
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
28_iqa_compliance: The use case is not broadly disseminating information to the public, so i don't this this is applicable.
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: The information to train\validate the model is obtainable from the Corporate Data Warehouse environment.
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
35_demo_features: Other
35_question_type: Service-Rated Disability Conditions
37_custom_code: No
38_code_access: Yes – source code is publicly available.
39_code_link: https://h2o.ai/platform/ai-cloud/make/h2o/
40_has_ato: Yes
41_system_name: Data and Analytical Support for Healthcare (DASH) - #2515
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: The design of DASH's intake is still to be determined because pending ATO is pending. The intake will occur at the facility level.
45_compute_request: Yes
47_timely_resources: Other
47_question_type: Two requests to ARCHES for a cloud-based development space have not yielded any response from their intake team.
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: PsychCorpCenter
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: The program is used to do multiculturally sensitive scoring of cognitive scores to make diagnostic impressions more accurate.
12_outputs: The information processed from the input of the providers on a specific Veteran/patient  and then a report is generated/processed from the information that was inputted.
16_dev_stage: Acquisition and/or Development
17_impact_type: Both
40_has_ato: No
2_use_case_name: ReflexAI
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Education & Workforce
10_commercial_ai: None of the above.
11_purpose_benefits: AI-powered simulations to provide nuanced, high-quality practice and real-time feedback to every individual undergoing training to serve as a responder on Veterans Crisis Line.
12_outputs: All information is accessible for training staff within the secure platform hosted on VA Enterprise Cloud. This data include simulation content and the instantaneous feedback across the scoring dimensions. This output includes insights on training effectiveness overall, as well as progress of individual trainees who are going through the training process.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/9/2022
19_date_acq_dev_began: 6/6/2023
20_date_implemented: 5/10/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 36C10B23F0271
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
35_demo_features: Other
35_question_type: This question does not apply to this use case
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: Hosted in ETIL, moving to Arches
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: 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: Using AI to enhance/augment classification of reported patient safety events, and to proactively identify patient safety vulnerabilities and potential areas of improvement
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: This system allows statisticians to identify trends and anomalies to proactively determine patient safety areas of improvement by identifying any new type of safety event and any abnormal increase in frequency of events and stop them before they become widespread.
12_outputs: Augmented event type classifications
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 8/5/2024
22_dev_method: Developed in-house.
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
28_iqa_compliance: Not applicable
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: TERA Memorandum Automation
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: OIT: Office of Information & Technology
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Toxic Exposure Risk Activity (TERA) Memorandum Automation utilizes natural language processing (NLP) based algorithms to provide recommended decisions to support completing the TERA Memorandum form, a form that is required to be completed for most Veteran Disability Claims. This functionality will allow approved Veteran Service Representatives (VSRs) to auto-populate certain questions within the TERA Memorandum form in VBMS using evidence stored in the Claims Evidence drastically reducing the amount of time needed to manually review documents in Claim Evidence (eFolder) and streamlines claim processing. The system currently leverages data from C&P’s Smart Search service and utilizes NLP-based algorithms to identify potential evidence to support recommended decisions on specific questions of the TERA memo. The specific output includes an open-text field response with confidence score and links to documents for manual review. The intended users of the system are VSRs who complete the TERA memo. 
12_outputs: Toxic Exposure Risk Activity (TERA) Memorandum Automation utilizes natural language processing (NLP) based algorithms to provide recommended decisions to support completing the TERA Memorandum form, a form that is required to be completed for most Veteran Disability Claims. This functionality will allow approved Veteran Service Representatives (VSRs) to auto-populate certain questions within the TERA Memorandum form in VBMS using evidence stored in the Claims Evidence drastically reducing the amount of time needed to manually review documents in Claim Evidence (eFolder) and streamlines claim processing. The system currently leverages data from C&P’s Smart Search service and utilizes NLP-based algorithms to identify potential evidence to support recommended decisions on specific questions of the TERA memo. The specific output includes an open-text field response with confidence score and links to documents for manual review. The intended users of the system are VSRs who complete the TERA memo.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 1/19/2024
19_date_acq_dev_began: 1/19/2024
20_date_implemented: 11/3/2024
40_has_ato: Yes
41_system_name: BIP Compensation and Pension User Interface ID: 2049