GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: WRDensity by Whiterabbit.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: 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/K202013.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: Workflow Box
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/pdf18/K181572.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: BTSSS 3542 Review
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: Inputting large amounts of data from paper forms into a digital system using AI.
11_purpose_benefits: The intended purpose of the AI model is to streamline the claims processing workflow by automatically extracting relevant information from patient-filled paper forms and organizing the corresponding scans chronologically. This automation eliminates the tedious task of manually renaming and sorting files, significantly reducing the time staff spend on these activities. The expected benefits include a marked decrease in manual labor hours, leading to lower operational costs and minimizing the need for overtime. Ultimately, this efficiency allows staff to focus more on patient care and other critical tasks, improving overall workflow productivity.
12_outputs: The AI model outputs a renamed version of the original file, moving it to a designated SharePoint folder with the relevant information extracted from the patient form. Additionally, specific columns in the file are updated with data to facilitate easy sorting by dates. This streamlined process ensures quick access to organized claims information and enhances overall efficiency.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
40_has_ato: Yes
41_system_name: BTSSS ID: 713
2_use_case_name: Withings Scan Monitor 2.0
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/K230812.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: HTM-LLM
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: RAG chatbot using technical service manuals to aid Healthcare Technology Management staff in medical device maintenance and troubleshooting
12_outputs: Chatbot responses
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 5/1/2024
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: No
33_agency_data: none, uses manufacturer service manuals
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://github.com/ahncs/AIE3/tree/main/Final%20Project
40_has_ato: No
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: still haven't provisioned VA resources yet.
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: WellDoc BlueStar
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/K190013.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: WAVE Clinical Platform
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/K171056.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: VX1, VX1+
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/K223516.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: Sybil - Lung Cancer Prediction Model by MIT
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 be used to personalize screening regime, calling high risk patients earlier to catch cancers in the early stages and potentially reducing screening burden for low risk patients.
12_outputs: It takes as input a lung low-dose CT scan, and produces a JSON return object with predictions of the odds of developing cancer within the next 6 years (one prediction for each year).
16_dev_stage: Acquisition and/or Development
17_impact_type: Both
18_date_initiated: 4/10/2024
19_date_acq_dev_began: 10/16/2023
22_dev_method: Developed in-house.
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: No
33_agency_data: We (Dayton HTM) have developed a web based front end to point DICOM images to the API. The resulting JSON cancer prediction results are transcribed into a excel table for validation. Our aim is to have the model on server behind the VA's isolation architecture (MDIA or SDAI) and have results stored in SQL database. With access secured via LDAP.
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
35_demo_features: Other
35_question_type: Low Dose CT Images of lung are exclusively used.
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: https://github.com/reginabarzilaygroup/Sybil
40_has_ato: No
41_system_name: Sybil
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Other
43_question_type: The system/model is isolated to one air-gapped workstation. Our aim is to have the system approved via the Enterprise Risk Assessment process. I will then request an isolated server to house the AI model.
45_compute_request: Yes
47_timely_resources: Other
47_question_type: The provisioning request via the EWR has not been started yet. The system as it stands now is still air-gapped.
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 published: Complete documentation has been published to a repository or data catalog within the agency and is made accessible to other data science teams for review and feedback.
2_use_case_name: eCaremanager (Philips)
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: eCaremanager integrates clinical, physiological, and demographic data from all patients entered into the National TeleCC Program.  The data are presented graphically to users.  Additionally, the data are used to generate acuity scores to assess each patients acuity in comparison to all other patients.  Trend and threshold alerts are also generated to notify providers to take a closer look at those patients and initiate treatments to remediate derangements and facilitate return to homeostasis and health.
12_outputs: The outputs of eCaremanager are acuity scores and trend and threshold alerts.  The software also integrates and presents patient care information to enable providers to care for larger number of patients and provide critical care at a population level.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 1/1/2010
19_date_acq_dev_began: 1/1/2010
20_date_implemented: 1/1/2010
40_has_ato: Yes
41_system_name: TCC ID: 2209
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: VUNO Med-DeepBrain
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/K231398.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: Policy AId
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: Policy lookup is a time-consuming task in the VA. This challenge was identified as an ideal candidate for generative AI product development. After completing AI engineering and product development training, our internal team developed a Retrieval-Augmented Generation (RAG) application that accepts policy-related questions and provides relevant answers based on Medical Center reference materials. 

Expected Benefits:
Time spent on finding/referencing facility policy decreases, increasing efficiency and employee satisfaction. 
Standardization of meeting minutes per Dayton governance structure.

12_outputs: LLM produces feedback in text with links to the associated medical center publications.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
2_use_case_name: Voluson Expert 22, Voluson Expert 20, Voluson Expert 18
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/K231965.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: Volta AF-Xplorer
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/K232616.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.