GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: Post discharge 30-day readmission or death prediction model to select patients for expedited postdischarge clinic follow-up.
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: The purpose of this AI is to act as an aid not a replacement for clinical decision-making about whether a patient should have clinical follow-up in the week following discharge (an accelerated timeframe) or follow-up in 2-4 weeks (the regular timeframe). The AI will consider dozens of factors to which a clinician does not have ready access or time to review during a typical discharge process. Patients that are predicted to have an adverse outcome will be recommended a quick follow-up but this will not be mandated, similarly patients not predicted to have an adverse outcome can still be scheduled for quick post discharge follow-up. Predicting which patients will die or be readmitted after discharge is a famously difficult unsolved clinical problem, and this tool will be one more data point for clinicians to aid but not replace clinical judgment during the discharge process.

The AI’s intended benefit is to make our healthcare system more systematic and accurate in identifying high-risk patients at discharge as a primary goal. An aspirational benefit is that this process of more systematic more accurate follow-up for high risk patients will lead to decreased re-admissions and mortality after discharge.
12_outputs: The XG boost system outputs a 0 and 100% probability of readmission or death within 30 days.
16_dev_stage: Acquisition and/or Development
17_impact_type: Both
18_date_initiated: 6/1/2023
19_date_acq_dev_began: 6/1/2023
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
33_agency_data: The AI training data set is our healthcare systems admissions and subsequent readmissions or death at 30 days between the fiscal years 2018 and 2023. This was derived from CDW data.
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: Marital Status, Age
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: More than 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.
2_use_case_name: RayStation 11B
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: FDA-cleared medical device to assist clinicians and improve healthcare outcomes.
12_outputs: Described in FDA documentation for this device (https://www.accessdata.fda.gov/cdrh_docs/pdf22/K220141.pdf).
16_dev_stage: Operation and Maintenance
17_impact_type: Both
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: Other
31_question_type: Not needed for this device.
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: 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: Other
43_question_type: Not applicable for this third party device.
45_compute_request: Other
45_question_type: Not applicable for this third party device.
47_timely_resources: Other
47_question_type: Not applicable for this third party device.
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.
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Enterprise Command Center - Generative AI 
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: The benefit of this use case is to provide plain English alert messages to service owners. The current alert language has proven suboptimal and results in a inordinate volume of alerts not being actioned. By feeding alerts into an LLM the message can be reconfigured to be understood by a human who in turn can take appropriate steps to resolve the alert.
12_outputs: Human readable alert messages
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 3/22/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: ServiceNow, SolarWinds, Splunk, Dynatrace, AppDynamics, and ScienceLogic all host alert data that would be used to train the model.
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: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
41_system_name: 2879, 2462, 2464, 2818, 2463
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Yes
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: 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: Adobe Creative Cloud 
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: Creating visually appealing presentations using AI-driven design suggestions.
11_purpose_benefits: Develop nursing education presentations 
12_outputs: Used to make animations or to populate AI art.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
40_has_ato: Yes
41_system_name: ASMS ID: 1279
2_use_case_name: QLAB Advanced Quantification Software
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: FDA-cleared medical device to assist clinicians and improve healthcare outcomes.
12_outputs: Described in FDA documentation for this device (https://www.accessdata.fda.gov/cdrh_docs/pdf20/K200974.pdf).
16_dev_stage: Operation and Maintenance
17_impact_type: Both
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: Other
31_question_type: Not needed for this device.
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: 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: Other
43_question_type: Not applicable for this third party device.
45_compute_request: Other
45_question_type: Not applicable for this third party device.
47_timely_resources: Other
47_question_type: Not applicable for this third party device.
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.
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Ambulatory heart rhythm monitoring
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: AI is used to analyze 14 days of heart rhythm to narrow down periods of abnormalities and identify such rhythms.
12_outputs: Its a report with various abnormalities identified and listed for the clinician to interpret and make recommendations.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
40_has_ato: No
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Insights Engine
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VEO: Veterans Experience Office
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: The Insights Engine provides a human-language interface to query aggregate-level / anonymized customer experience and operational metrics and data in support of VA research and design initiatives. This tool leverages large language model interpretation and agents to write queries for structured and unstructured data, and articulate responses based on relevant source review or Retrieval Augmented Generation (RAG).

This tool will serve as a research librarian to empower research and design teams across the VA enterprise to identify and leverage past-relevant learnings that reduce redundant research initiatives and build on prior work and knowledge. Staff can inquire about what has been done / is known about x, y, z and receive answers in plain language.
12_outputs: Prior research findings, qualitative and quantitative insights.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 4/1/2024
19_date_acq_dev_began: 4/1/2024
2_use_case_name: Philips EPIQ Diagnostic Ultrasound System, Philips Affiniti Diagnostic Ultrasound System
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: FDA-cleared medical device to assist clinicians and improve healthcare outcomes.
12_outputs: Described in FDA documentation for this device (https://www.accessdata.fda.gov/cdrh_docs/pdf21/K212704.pdf).
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 11/28/2022
19_date_acq_dev_began: 11/28/2022
20_date_implemented: 11/28/2022
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: Other
31_question_type: Not needed for this device.
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: 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: Other
43_question_type: Not applicable for this third party device.
45_compute_request: Other
45_question_type: Not applicable for this third party device.
47_timely_resources: Other
47_question_type: Not applicable for this third party device.
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.
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Glassbeam Clinsights 
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: Identifying unusual patterns in system logs from a single incident report using AI.
11_purpose_benefits: The benefits of this AI is to gather medical device data, and use machine knowledge to attempt prediction of when medical equipment may fail. 
12_outputs: Gathers medical device hardware information and compares the data that was gathered to determine if it is in normal range, declining, or failed to alert the technicians as soon as possible to repair/replace faulty parts before they are used in a medical procedure.
16_dev_stage: Implementation and Assessment
17_impact_type: Both
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: VX Insights Hub Agent
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VEO: Veterans Experience Office
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: The VX insights hub provides a human-language interface to query aggregate-level / anonymized customer experience and operational metrics and data in support of VA performance improvement and decision-making. This tool leverages large language model interpretation and agents to write queries for structured and unstructured data, and articulate responses based on relevant source review or Retrieval Augmented Generation (RAG).

This tool will empower VA staff in decision-making and performance improvement roles, who may not have a background in data science, statistics or analytics, to inquire about performance and experience and receive answers in plain language.
12_outputs: Natural language responses to CX and operations questions + validation information about the query that was written and run, etc.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 2/1/2024
19_date_acq_dev_began: 3/1/2024
2_use_case_name: OPTIS Mobile Next Imaging System, OPTIS Integrated Next Imaging System
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: FDA-cleared medical device to assist clinicians and improve healthcare outcomes.
12_outputs: Described in FDA documentation for this device (https://www.accessdata.fda.gov/cdrh_docs/pdf21/K210458.pdf).
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 7/17/2023
19_date_acq_dev_began: 7/17/2023
20_date_implemented: 7/17/2023
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: Other
31_question_type: Not needed for this device.
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: 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: Other
43_question_type: Not applicable for this third party device.
45_compute_request: Other
45_question_type: Not applicable for this third party device.
47_timely_resources: Other
47_question_type: Not applicable for this third party device.
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.
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Compliance Made Easy (CME) Cloud Optimization
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: OIT: Office of Information & Technology
8_topic_area: Other
8_question_type: This may fall under government services, but this is directly related to supporting the office of the executive secretary with official communications.
10_commercial_ai: Improving the quality of written communications using AI tools.
11_purpose_benefits: The purpose is to provide automated review of official communications against the approved VA style guide, tone, and other key elements to making communications to the public effectively. The goal is to reduce the amount of time needed to create and review the documents leading to saved time and money. The benefits of using AI include more intelligent control over a rule based system, low/no code updates to the model through direct NLP prompts to update the approved style guides, leveraging machine learning to improve communications and tone over time, less reliance on human intervention to review and edit communications. 
12_outputs: AI outputs will include updated communications style and formatting based on the style guide, suggested language to improve tone, readability and conciseness of communications, and data based on the model running.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 9/15/2024
2_use_case_name: O-arm O2 Imaging System
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: FDA-cleared medical device to assist clinicians and improve healthcare outcomes.
12_outputs: Described in FDA documentation for this device (https://www.accessdata.fda.gov/cdrh_docs/pdf24/K240465.pdf).
16_dev_stage: Operation and Maintenance
17_impact_type: Both
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: Other
31_question_type: Not needed for this device.
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: 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: Other
43_question_type: Not applicable for this third party device.
45_compute_request: Other
45_question_type: Not applicable for this third party device.
47_timely_resources: Other
47_question_type: Not applicable for this third party device.
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.
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: NeuroQuant
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: FDA-cleared medical device to assist clinicians and improve healthcare outcomes.
12_outputs: Described in FDA documentation for this device (https://www.accessdata.fda.gov/cdrh_docs/pdf17/K170981.pdf).
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 10/31/2017
19_date_acq_dev_began: 10/31/2017
20_date_implemented: 10/31/2017
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: Other
31_question_type: Not needed for this device.
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: 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: Other
43_question_type: Not applicable for this third party device.
45_compute_request: Other
45_question_type: Not applicable for this third party device.
47_timely_resources: Other
47_question_type: Not applicable for this third party device.
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.
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Master Claims Assistance Tool (M-CAT)
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VBA: Veterans Benefits Administration
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Function of the system is generative AI use and derivatives to assist VA personnel. Inputs to the model include policy, procedure, and other knowledge management documents such as CFR database knowledge and user entered inquiries. 
12_outputs: Outputs include RAG model results from chatbot. Intended users are VA or VBA staff as directed by SES.
16_dev_stage: Acquisition and/or Development
17_impact_type: Rights-Impacting

18_date_initiated: 8/1/2024
19_date_acq_dev_began: 8/1/2024
51_extension_request: Yes – Agency requested an extension for this use case.