GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: ECG/EKG Machines- Interpretation of Results
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: This software aids with interpretation of  clinical Electrocardiogram tracings for VA clinicians. 
12_outputs: The system integrates w/ CPRS and outputs patient demographics on the ECG print-out.  It then prints a list of notable findings from the ECG on the form that is later reviewed and confirmed or overturned by a licensed provider.   In many cases, the machine interpretation is correct and is confirmed by the provider.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 2/1/2022
19_date_acq_dev_began: 4/1/2022
20_date_implemented: 10/1/2022
22_dev_method: Developed in-house.
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
28_iqa_compliance: This is a commercial OTC system that has been validated in clinical use over a period of years.  It is compliant w/ VA OIT guidelines as it does operate on VA's network and interfaces with CPRS.
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Other
31_question_type: The AI algorithm is validated by the manufacturer before the product is approved for use.
33_agency_data: none
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
35_demo_features: Sex/Gender, Age
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Macview 360
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: Unit is OTS ready.  No provisioning is needed.
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: Appointment Comments Categorization
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: OIT: Office of Information & Technology
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: To deliver additional timely options for patients that are having mental or physical issues during appointment signup.
12_outputs: Right now, I'm focusing on alerting around mental health, so the model will return, "Timely_Alert_Needed, 0.xxxxx"
16_dev_stage: Acquisition and/or Development
17_impact_type: Safety-impacting
18_date_initiated: 8/1/2024
19_date_acq_dev_began: 8/1/2024
22_dev_method: Developed in-house.
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
28_iqa_compliance: 

We are starting that process now.
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
33_agency_data: 

Comment data in cdw prod
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
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: Summit
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Other
45_question_type: 

Working with Summit now.
47_timely_resources: No
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: 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.
2_use_case_name: TeraRecon
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/K220349.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: Ambient AI scribe
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: Transcribing and summarizing a recorded meeting or interview using AI.
11_purpose_benefits: Human scribes have been effective toward decreasing clinical documentation burden, increasing physician satisfaction, and decreasing physician time spent in the EHR during patient visits. However, notes written by humans are variable in quality. In addition, scribes are costly, requiring time to train and have high turnover. Ambient AI medical scribes and solutions to automate document processing show promising as potential solutions to reduce physician burnout. VA recently completed an AI Tech Sprint in this space and intends to pilot several of these solutions at VA Medical Centers. 
12_outputs: The system produces written summaries of clinical encounters
16_dev_stage: Initiated
17_impact_type: Both
18_date_initiated: 5/21/2024
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Limited Use of Azure Speech Services in PETALS 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: We have specific and limited use of Azure Speech Services in PETALS Platform, where Azure Speech Services will read either (a) a participant first name or (b) a numerical step count, and then read-aloud that content to validate the user and information input provided by user during program engagement. 
12_outputs: Audio verbalization (speak-aloud during telephone engagement) of a first name and of a numerical step count.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 6/14/2021
19_date_acq_dev_began: 11/17/2023
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 36C250P2111
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: No
33_agency_data: Training is not applicable in this case given that the 'data' used is provided to the system. Extensive testing has been done to validate/evaluate accuracy of speech services during extensive testing using test data.
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
35_question_type: NONE
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: Patient Engagement Tracking and Longterm Support and PETALS CS (Communication Services) - (eMASS 1341 and 2589)
42_dev_tools_wait: 6-12 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: 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: Medallia SaaS - VSignals and ESignals
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VEO: Veterans Experience Office
8_topic_area: Other
8_question_type: Medallia is a customer experience management platform which can leverage AI (NLP) to review survey comments for actioning.
10_commercial_ai: None of the above.
11_purpose_benefits: Medallia is a customer experience management platform which can leverage AI (NLP) to review survey comments for actioning.   VSignal survey comments (from Veterans) are processed by NLP to HELP determine if a Veteran is potentially in risk of crisis (related to homelessness and mental health).   If NLP flags a comment as a potential risk, a case is created and sent to the Crisis Line and Homeless Program for review and actioning.
12_outputs: See above
16_dev_stage: Operation and Maintenance
17_impact_type: Safety-impacting
18_date_initiated: 1/1/2017
19_date_acq_dev_began: 6/1/2017
20_date_implemented: 1/1/2018
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: VA-22-00077985  this is the PIID for Call Order 25 
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Data from VHA's corporate data warehouse
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: Race/Ethnicity, Sex/Gender, Age, Other
35_question_type: rural - urban
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: Medallia eCloud-e
43_infra_provisioned: Other
43_question_type: The infrastructure was available in 2017 so unclear if it went thru centralized intake
45_compute_request: Other
45_question_type: the use case utilizes SAAS cloud product so not sure if the question is applicable
47_timely_resources: Other
47_question_type: see above
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: AGFA Dose Monitor 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/pdf18/K180589.pdf).
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 12/28/2023
19_date_acq_dev_began: 12/28/2023
20_date_implemented: 12/28/2023
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: Volpara Imaging Patient Hub
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/K211279.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: Identity Governance and Administration (IGA)
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: Identifying unusual patterns in system logs from a single incident report using AI.
11_purpose_benefits: The purpose and expected benefits of the AI are to reduce risks within VA workforce regarding access management. For example, the IGA tool will generate risk scores for access requests, allowing automation to approve low risk requests automatically while flagging others that are high-risk, sensitive, or critical for manual review. 
12_outputs: The system will display risk scores on a dashboard, send email notifications about access requests needing to be reviewed, or recommend access certifications/reviews.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
2_use_case_name: VET-HOME Contact Center AI 
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: OIT: Office of Information & Technology
8_topic_area: Health & Medical
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: We're looking to explore the use of AI to give VA customer service agents better situational awareness to a Veteran through the use of AI tools to summarize and contextualize queries received through our contact centers.  
12_outputs: We'd like to train custom co-pilots on data used by the contact center CRMs, to be able to proactively pull up information relevant to the conversation and the Veteran's queries.  The output would be a window that would display information relevant to the Veterans needs at that time based on the contact center they are working with.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 9/23/2024
2_use_case_name: Beckam Coulter DxH 800
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/pdf12/K120771.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: UIPath Document Understanding
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: The tool enables Optical Character Recognition and data extraction from submitted documents through AI document processing.  This tool can extract data accurately and efficiently to allow automations to input or use the data for other applications.  
12_outputs: UiPath's Document Understanding will provide computerized information from forms and documents (computer generated or handwritten) that can be used by the automation to add information to other applications, or present to assist in decisions by the Agents for Veterans.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/23/2023
19_date_acq_dev_began: 7/23/2023
20_date_implemented: 11/15/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: 36C10B23F0307, NNG15SD66B
24_hisp_support: No
40_has_ato: Yes
41_system_name: VA Robotic Process Automation Platform 2755
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.
2_use_case_name: Potential Fraud or Waste
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: OM: Office of Management
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The Purchase Card dashboards are a suite of dashboards that allow various purchase card managers to monitor purchase card spending of employees. The dashboards help monitor for fraud, waste, and abuse, and assist in providing oversight for compliance with purchase card laws and policies. We have now incorporated advanced data analytics within the dashboard. The Advanced data Analytics model generates an expected received date for items. It then flags items that have gone beyond the expected generated date by flagging items that were received late (possible waste) and/or items that have not been received yet (possible fraud). This has the potential to save the analyst time by investigating cases that have been flagged as opposed to investigating potentially an entire dataset, which is what they have been doing up until now. The model analyzes all purchase card transactions including ordered, delivery, and recieved dates as its inputs. It outputs expected recieved dates. The intended users of the product are purhase card managers. 
12_outputs: The Advanced data Analytics model generates an expected received date for items. It then flags items that have gone beyond the expected generated date by flagging items that were received late (possible waste) and/or items that have not been received yet (possible fraud).
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 4/1/2024
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: Yes
33_agency_data: Purchase card transaction details
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
35_demo_features: Sex/Gender, Other
35_question_type: No demographic information is used.
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: 6-12 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: 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.