GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: FastStroke, CT Perfusion 4D
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/K193289.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: Koios
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: Used with Automated Breast Ultrasound (ABUS). Classifies breast ultrasound lesions and automates reporting for improved efficiency and streamlined workflows. Increased sensitivity and specificity with reduced variability. Reduces unnecessary treatments (existing BI-RADS 3's can be accurately and safely downgraded) and provides probability of malignancy aligned to BI-RADS categorization. 
12_outputs: Classifies breast ultrasound lesions. 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).
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: Using AI to read PDFs and parse the information to SharePoint list
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: Inputting large amounts of data from paper forms into a digital system using AI.
11_purpose_benefits: The AI will read PDFs from the TPA, and parse the data into a sharepoint list, reducing the employees processing time by 20%
12_outputs: SharePoint, and will be used in Power BI.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 5/29/2024
2_use_case_name: Discovery MI Gen2
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/K211846.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: Genius 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: Mammography. Assists the Radiologist in locating lesions likely representing breast cancer by searching each slice of the tomosynthesis image set. Lesions are marked on the appropriate slices that can be overlaid on a synthesized 2D image and on 3DQuorum SmartSlices. These 2D overlays augment Radiologists by providing an overview image with suspicious areas indicated with quick navigation to the tomosynthesis slice that the mark was originally identified on. 
12_outputs: Lesions are marked on identified imaging slices with reference back to corresponding tomosynthesis slice.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 1/1/2019
19_date_acq_dev_began: 1/1/2021
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: 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: 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/pdf14/K141480.pdf).
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 8/2/2024
19_date_acq_dev_began: 8/2/2024
20_date_implemented: 8/2/2024
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: Discharge Predictive Model
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: Implementation of a predictive model that can identify acute inpatients likely to discharge within 24 hours can facilitate communication across clinical and ancillary staff teams who are otherwise currently reliant on a manual process that requires clinical teams to enter a Pre-Discharge order, a process which can be inconsistent due to trainee turnover. The expectation of this project is increased awareness and communication of the hospital's anticipated aggregate discharge volume resulting in potentially improved visibility on hospital throughput for bed coordination, including improved capability and flexibility to accommodate additional hospital admissions and transfers.
12_outputs: The model estimates the overall clinical readiness of inpatient patients using fundamental parameters including vital signs and lab result trends and then produces an output that aggregates the total anticipated number of patients across the facility that would be expected to be ready for discharge within 24 hours based on basic clinical criteria.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 6/21/2023
19_date_acq_dev_began: 6/21/2023
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: GS-35F-0086U, 36C10B21F0353
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: VHA Corporate Data Warehouse for Facility / Station 600 (VA Long Beach Healthcare System). Data types included in Training and Development: • Admission Date • Discharge Date • Laboratory Results (Basic and comprehensive metabolic panel, Complete Blood Count) • Vital Signs (Blood Pressure, Heart Rate, Pulse Oximetry, Temperature, Height, Weight, Pain rating) • Inpatient Specialty Service • Age • Race • Ethnicity • Gender
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: Corporate Data Warehouse (VASI ID #1152)
42_dev_tools_wait: 6-12 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: 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: CT CoPilot
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/pdf16/K161322.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: Cranial Navigation, Navigation Software Cranial, Navigation Software Craniofacial, Cranial EM System, Automatic Registration iMRI
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/K223288.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: Aquilion ONE (TSX-305A/6) V8.9 With AiCE
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: AI Deep Learning CT Reconstruction
12_outputs: Described in FDA documentation for this device (https://www.accessdata.fda.gov/cdrh_docs/pdf18/K183046.pdf).
16_dev_stage: Implementation and Assessment
17_impact_type: Both
18_date_initiated: 5/12/2023
19_date_acq_dev_began: 5/12/2023
20_date_implemented: 5/12/2023
23_contract_piids: 644-B08013
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
33_agency_data: 
CT scans

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: Age, Sex/Gender, Race/Ethnicity, Other
35_question_type: anatomy
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: MD-LITE ID: 2278
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Other
43_question_type: Controlled by OEM, Canon Medical.
45_compute_request: Other
45_question_type: Controlled by OEM, Canon Medical.
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: CellaVision DM1200 with the body fluid application
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/pdf10/K102778.pdf).
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 8/22/2023
19_date_acq_dev_began: 8/22/2023
20_date_implemented: 8/22/2023
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: CARTO 3 EP Navigation System Software V8.0 (FG-5400-00, FG-5400-00U)
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/pdf23/K231207.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.