GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: MAGNETOM Vida; MAGNETOM Lumina; MAGNETOM Aera; MAGNETOM Skyra; MAGNETOM Prisma; MAGNETOM Prisma fit
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/scripts/cdrh/cfdocs/cfPMN/pmn.cfm?ID=K231560).
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: 3D Quorum 
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: Mammography. AI-powered analytics - Generates 6mm 'SmartSlices' from the original high-resolution 3D data. Identifies clinically relevant regions of interest through overlapping slices (3mm) to ensure no loss of 3D data. Reduces the number of 3D images to be reviewed by the Radiologist by 2/3. 
12_outputs: Produces 6mm 'SmartSlices' from native 3D data to highlight clinically relevant or potentially clinically relevant regions of interest. Augments the decision making process of Board Certified or Board Eligible Radiologists. Interpreting Radiologists retain the ability to 'accept' or 'reject' the suggested outputs.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 1/1/2019
19_date_acq_dev_began: 1/1/2020
20_date_implemented: 1/1/2021
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
28_iqa_compliance: This use case doesn't appear to meet the definition in M-19-15 as disseminating 'influential scientific, financial, or statistical information.'
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: No
33_agency_data: This use case will access PHI/PII from VA 'Home of Record' systems (Vista/CPRS) and peripheral Radiology PACS & Dictation Systems.
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: More than 12 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: 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: INtuition-Structural Heart Module
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/scripts/cdrh/cfdocs/cfPMN/pmn.cfm?ID=K191585).
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: Intelligent 2D 
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: Mammography - Produces synthetic 2D images from 3D mammography data to improve the detection of breast lesions while reducing radiation dose. Reduces the 'noise' if the images, helping to eliminate random elements that may distort the image. Provides Interpreting Radiologists improved detection ability through reduction of artifacts that may reduce visibility of lesions - ultimately providing a clearer picture of breast tissue. FDA Approved. 
12_outputs: Provides synthetic 2D image to augment the Interpreting Radiologist's ability to detect breast lesions.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 1/1/2019
19_date_acq_dev_began: 1/1/2020
20_date_implemented: 1/1/2021
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
28_iqa_compliance: This use case doesn't appear to meet the definition in M-19-15 as disseminating 'influential scientific, financial, or statistical information.'
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: No
33_agency_data: This use case will access PHI/PII from VA 'Home of Record' systems (Vista/CPRS) and peripheral Radiology PACS & Dictation Systems.
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: More than 12 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: 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: QV CAD
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: Automated Breast Ultrasound (ABUS) solution from QView Medical. Aids Radiologists with interpreting screening procedures for women who have negative mammograms but have had a 3D breast ultrasound due to dense breast tissue. Receives input images via standard DICOM format. Native imaging from the ABUS together with the output of the QVCAD images are displayed on the viewer. QVCAD engine uses several image patter recognition processes and artificial neural networks to detect suspicious areas in the breast with the objective to distinguish potential breast lesions from normal breast tissue. 
12_outputs: Detects suspicious or potentially suspicious areas in the breast.  Augments the decision making process of Board Certified or Board Eligible Radiologists. Interpreting Radiologists retain the ability to 'accept' or 'reject' the suggested outputs.
16_dev_stage: Acquisition and/or Development
17_impact_type: Both
18_date_initiated: 10/1/2023
19_date_acq_dev_began: 9/6/2024
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
28_iqa_compliance: This use case doesn't appear to meet the definition in M-19-15 as disseminating 'influential scientific, financial, or statistical information.'
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: No
33_agency_data: This use case will access PHI/PII from VA 'Home of Record' systems (Vista/CPRS) and peripheral Radiology PACS & Dictation Systems.
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: More than 12 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: 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: FlexLine: Detect patterns and make corrective action recommendations in application and dependency time to mitigation data. 
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: Within the FlexLine materials management application, this AI capability would look at patterns across the time-to-mitigation data and potentially other scans for application vulnerabilities. If teams are not resolving vulnerabilities within the 30, 45, 60 day periods, then it could expose our products and degrade Veteran care. The goal would be to connect the duration to mitigate to other available data trends and recommend actions to the application teams that could help them resolve the issues faster.
12_outputs: Will provide detailed text data hat combines/correlates scan results for the app team and what might be causing the inability to resolve. This additional intelligence and recommended resolution(text) could be provided to the app team through automated Jira tickets to help them understand a way to resolve the issue.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 9/19/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 36C10G24D0048, 36C10G24N0112
24_hisp_support: No
27_public_info: No
28_iqa_compliance: Not Applicable
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: OCC FlexLine vulnerability and scan 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: Other
35_question_type: No demographic variables are used. Scan result data and variables are not tied to individuals.
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: 2313
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Other
47_question_type: We've not made a request to the relevant data science groups as we are building resource provisioning, and MLOps capabilities first.
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: Medical Imaging Auto-segmentation
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: To make the process of drawing regions of interest more efficient 
12_outputs: Rather than a physician manually outlining a lesion or organ such as the liver the AI system uses imaging data and anatomic atlas information to create a proposed outline that the physician can then adjust or approve for use
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: FFRangio
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/pdf19/K192442.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: Quantra 
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: Mammography. Uses multi-class support vector machine (SVM) based classification technique to segregate breast types into four categories based on breast parenchymal tissue pattern and texture representation to assist Radiologists in interpretations and reduce intra- and inter-reader variability. 
12_outputs: Classifies breast type. Augments the decision making process of Board Certified or Board Eligible Radiologists. Interpreting Radiologists retain the ability to 'accept' or 'reject' the suggested outputs.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 1/1/2019
19_date_acq_dev_began: 1/1/2020
20_date_implemented: 1/1/2022
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
28_iqa_compliance: This use case doesn't appear to meet the definition in M-19-15 as disseminating 'influential scientific, financial, or statistical information.'
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: No
33_agency_data: This use case will access PHI/PII from VA 'Home of Record' systems (Vista/CPRS) and peripheral Radiology PACS & Dictation Systems.
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: More than 12 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: 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: VSTOne – AI-Driven Patient Monitoring and Care Delivery
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: Supporting Care Through AI

The healthcare industry is at a critical juncture due to the convergence of rising patient demands and severe staffing shortage, specially in nursing. According to the U.S Bureau of Labor Statistics projection in 2022, American hospitals will be hiring 1,100,00 registered nurse positions. In response to this challenge AI driven patient monitoring care delivery can proactively assist nurses.
The integration of artificial intelligence into healthcare presents a unique opportunity to enhance patient outcomes while supporting clinical staff in an increasingly challenging environment. With the looming shortage of nursing staff and the growing demand for patient care, there is an urgent need for technological solutions that support nurses in their roles. Today, I present VSTOne, an innovative AI solution designed to assist nurses, prevent patient falls, and improve care quality through continuous monitoring and real-time alerts.
12_outputs: Supporting Care Through AI VSTOne leverages advanced AI to provide comprehensive monitoring solutions that address several critical areas of patient care: • Assisting Nurses: Automated monitoring allows nurses to focus on higher-level care tasks. • Preventing Falls: The system predicts potential falls 30-65 seconds in advance, allowing timely intervention. • Preventing Pressure Ulcers: Early detection of pressure ulcer risks through continuous monitoring of patient movement. • Telemetry and Telehealth: Vitals are tracked via wearable patches, providing real-time data to clinicians, and enabling telehealth consultations.
16_dev_stage: Initiated
17_impact_type: Both
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name:  Surveillance and Reporting of Suicidal Ideation Assessment in PTSD Specialty Care Clinical Notes using Natural Language Processing (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: We endeavor to surveil and monitor nationally both the conduct and quality of suicide ideation (SI) assessment during treatment in PTSD specialty care. We hope eventually to increase the consistency and quality of SI assessment in PTSD care to enhance clinical management of suicide risk. 
12_outputs: Our methods entail the use of natural language processing (NLP) of progress note documentation of treatment within the PTSD Clinical Team (PCT) specialty care clinics, to identify SI mentions, to quantify SI assessment documentation, and to classify SI mention clinical quality. We will provide reports of consistency and quality and the national, VISN, and medical center levels.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 8/1/2023
19_date_acq_dev_began: 10/1/2023
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: VA Corporate Data Warehouse (CDW) provides clinical progress notes in text form through its TIU for our use in this project.
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: Other
35_question_type: The NLP machine learning and rule-based processes rely solely on clinical progress note text to identify suicide ideation assessment, and do not utilize demographic variables in its machine learning/rule-based model.
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: CDW
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.