GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: LINQ and  LINQ II Loop Recorder 
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: Implantable heart monitoring device. This continuously checks the heart rhythm and rate.  
12_outputs: The information (recordings) are remotely transmitted and recorded for interpretation by a Medical provider.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 12/21/2021
19_date_acq_dev_began: 12/7/2021
20_date_implemented: 12/27/2021
22_dev_method: Developed with contracting resources.
23_contract_piids: Sara Lind Boston Scientific Peripheral Interventions Field Inventory Management sara.lind@bsci.com P: 763.255.0037
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: No
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: Sex/Gender, Age
37_custom_code: No
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: Yes
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.
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: VA.gov Chatbot: Use of AI for summative and/or intent classification of Va.gov content
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: None of the above.
11_purpose_benefits: The VA.gov Chatbot currently uses Natural Language Understanding (primarily driven by Microsoft Power Virtual Agents (PVA) and Custom Question Answer (CQA)) to interpret Veterans' intent and match it to responses within the knowledge base.

The future state vision for the VA’s chatbot solution involves leveraging advanced AI services and capabilities to increase the breadth of questions that the Chatbot will be able answer, to improve the accuracy of each answer, and in cases of necessary channel escalation, to accurately match user utterances to contact center routing queues.
12_outputs: Input: Understanding user intent: Copilot can determine what the user is trying to accomplish based on their query. Output: Generating relevant responses: The AI can then provide a suitable response or action based on the user's intent.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/1/2021
19_date_acq_dev_began: 9/1/2021
20_date_implemented: 3/2/2022
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: VA118-16-D-1015,36C10B21N10150056
24_hisp_support: No
27_public_info: Yes
28_iqa_compliance: The Chatbot is open to the public. In the above, we interpret "Disseminate" to refer to ability of the Chatbot to provide information that appears on VA.gov to users who ask general questions of it.
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Customer Experience Insights (CXI) data lake - all chatbot transcripts are written here and leveraged in analysis
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: Yes
41_system_name: VA.gov Chatbot
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
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: ZIO PATCH HEART MONITOR
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: Records the heart rate and rhythm for up to 14 days to diagnose arrhythmias and any other electrical conduction issues.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
40_has_ato: Yes
41_system_name: MD-LITE ID: 2278
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Caller intent classification using Google Dialogflow (VoiceBot)
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: This project is part of the Unified Communications goal of modernizing call centers. AI (as NLU/NLP) can expedite triage, improve caller-to-agent matching, and handle routine tasks (through APIs) without agent support. 
12_outputs: Mainly conversational output. The VoiceBot will act as a caller-concierge, helping Veterans locate the right agent and/or solve tasks (e.g., claims status checks). Conversational scripts, including intents, entities and slots will be stored in CXI and/or other databases (e.g., Clarabridge) standard to VA call centers today.
16_dev_stage: Acquisition and/or Development
17_impact_type: Safety-impacting
18_date_initiated: 7/1/2022
19_date_acq_dev_began: 12/1/2022
2_use_case_name: Ordr
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: Ordr SCE Analytics employs advanced artificial intelligence and machine learning (AI/ML) technologies to further assist in the classification of local networked devices required for accurate device inventory. AI/ML enhances and accelerates the classification process by identifying specific behavior patterns that are unique to certain devices. These passive classification techniques are minimally invasive and do not rely on active scanning methods which are often disruptive to sensitive business-critical IoT devices.
12_outputs: Only network device metadata is forwarded to the SCE Analytics server. Sensitive data such as PHI is immediately discarded so that only metadata which contains information pertinent to device identification is forwarded.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 11/2/2021
19_date_acq_dev_began: 3/7/2022
20_date_implemented: 6/27/2024
40_has_ato: Yes
41_system_name: ORDR
2_use_case_name: Machine Algorithm for Report Surveillance (MARS)
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: OEHRM: Office of Electronic Health Record Modernization
8_topic_area: Health & Medical
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: The purpose of the MARS tool is to identify end-user submitted federal EHR incident reports that are likely to affect patient safety.  The MARS tool will augment the existing manual incident report review process.       
12_outputs: The output of the system is a list of ServiceNow tickets that require human review to validate the MARS determination that the issue described in the ticket is a patient safety issue.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 9/1/2022
2_use_case_name: Pharmacy AI Managed Inventory System (PHAIMIS)
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: PHAIMIS will be an AI-powered inventory management system to centralize and optimize inventory processes within the VA Pharmacy Service. This system will integrate AI and data analytics to streamline inventory management, provide real-time notifications, and enhance user experience through seamless integration into existing workflows.
12_outputs: System outputs will include demand forecasting, procurement strategy optimization, and dynamic inventory thresholds.  It may also include anomaly detection alerts and other features.
16_dev_stage: Initiated
17_impact_type: Safety-impacting
18_date_initiated: 5/21/2024
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Other
31_question_type: We do not yet have access to ALL data, such as vendor supply data, but not all data is necessary to create a solution that provides value.  We do have access to all data required for forecasting filled prescriptions, for example, and this alone provides value if the scope of the use case needs to be narrowed accordingly.
33_agency_data: Prescription fill time-series data (note: PHI/PII not needed) needed for demand forecasting.  Pharmacy inventory data needed for ordering recommendations.
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: Yes
40_has_ato: Yes
41_system_name: Summit Data Platform
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
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: Silverberry Surgery Planning AI
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: Assist in planning and operations in the perioperative process including sps, supplies, personell requirements and patinet planning. 
12_outputs: Output will be multiple AI agents responsible for various tasks related to the peri operative process.
16_dev_stage: Acquisition and/or Development
17_impact_type: Both
18_date_initiated: 9/26/2024
19_date_acq_dev_began: 9/26/2024
22_dev_method: Developed with contracting resources.
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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. 
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: 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: 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.
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: ICU CIS/ARK PDF Medical Entity and Clinical Scoring Extraction (PHI 3.5 LLM)
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: Extract clinical data elements from unstructured clinical note text into structured and semi-structured formats, mapping to standardized terminologies, thus making unstructured note text available to downstream analytics processes and clinical information systems.
12_outputs: Extracted data elements in structured and semi-structured formats, and mappings to standardized terminologies such as SNOMED, LOINC, ICD-10 and RxNorm.
16_dev_stage: Acquisition and/or Development
17_impact_type: Safety-impacting
18_date_initiated: 6/1/2023
19_date_acq_dev_began: 6/3/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: TIU notes, CISARK PDF documents and other unstructured and semi-structured text-based patient care records
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
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: Arches
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
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: BlueTeam AI
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: Identifying unusual patterns in system logs from a single incident report using AI.
11_purpose_benefits: BlueTeam AI enables network monitoring capabilities to provide governance and guardrails for LLM Generative AI use for the enterprise.
12_outputs: The system outputs only approved data and data categories to LLM as well as outputs a return notification to the user for any violations.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: OAWP Investigative, Analytics, and Reporting Capabilities
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: OAWP: Office of Accountability and Whistleblower Protection
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Transcribing and summarizing a recorded meeting or interview using AI.
11_purpose_benefits: We are in the initial phases of prototyping an audio transcription application using Azure AI Speech Services.  The goal of this project is to provide a streamlined means for OAWP investigators to receive transcripts of subject interviews.
 
Additionally, OAWP's Compliance Analytics and Reporting Division has long-term goals to use a supervised machine learning model to classify external oversight entity reports.  They are also evaluating opportunities to use deep learning models to explore data, and identify trends and anomalies.
12_outputs: Enhanced Analytics, Reporting, and Transcription Services.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 6/1/2024
2_use_case_name: Multi-Modal Digital Image Exchange - AI (MDIE-AI)
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: It creates an AI driven orchestration layer and generative AI summary for  automating computer vision models with saved dicom diagnostic images.
12_outputs: The system will output the results of each model into a generated summary PDF that is available for review directly from the dicom file.
16_dev_stage: Acquisition and/or Development
17_impact_type: Safety-impacting
18_date_initiated: 1/22/2024
19_date_acq_dev_began: 2/19/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
28_iqa_compliance: maintain data within current ATO environments.
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: radiology images, reports, problem list, demographics
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_demo_features: Race/Ethnicity, Sex/Gender, Age
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: 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: 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: VA Supply Chain Knowledge Management Dashboard
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: Searching for information using AI.
11_purpose_benefits: The goal is to leverage already existing Power BI tools to enhance search and learning capabilities. 
The Power BI dashboards are for internal VALOR mission topics that require timely and more precise knowledge management (KM) and lessons learned output from publicly available documentation (ex. congressional reports). 

THIS CAPABILITY IS NOT RIGHTS OR SAFETY IMPACTING AS IT DOES NOT ACCESS ANY INDIVIDUALS INFORMATION.
12_outputs: This involves enabling a knowledge management strategy that includes advanced data analysis tools (AI and ML) from Power BI stakeholder engagement for validation, dynamic dashboards for knowledge sharing, and continuous improvement processes. This strategy ensures that knowledge is effectively captured, validated, shared, and utilized to drive continuous improvement and mitigate risks.   The expected impact includes internal mission use by VALOR for improved risk mitigation, enhanced collaboration, and continuous process improvement.  And the overall outputs are only data provided by the public documents.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/24/2024
19_date_acq_dev_began: 8/2/2024
2_use_case_name: Clinical outcomes for asynchronous teledermatology
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: Determine clinical outcomes of patients with skin disease for research and QI purposes.
12_outputs: Classify dermatology progress notes into 6 classes:  Resolved, improved, unchanged (clinically significant), unchanged (not clinically significant), worse, and No outcome stated.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 6/9/2020
19_date_acq_dev_began: 6/1/2022
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: Yes
33_agency_data: Clinical notes and encounter data from CDW
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: https://www.llama.com/
40_has_ato: No
41_system_name: VINCI
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
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: Lyssn for Mental Health
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: To improve fidelity to evidence based practices in Behavioral Health, such as Motivational Interviewing. 
12_outputs: Lyssn uses AI algorithms to automatically generate clinical quality metrics from a recording (or  transcript) of a provider-patient session or appointment. Metrics range from general counseling  and patient-centered communication to fidelity to evidence-based counseling, including  Motivational Interviewing and Cognitive Behavioral Therapy.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 10/3/2022
19_date_acq_dev_began: 6/15/2023
20_date_implemented: 9/5/2023
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
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: Ability Status
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
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: 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.