GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: XtractOne
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Law & Justice
10_commercial_ai: None of the above.
11_purpose_benefits: The new WDS will allow the VA Police to centralize all the facilities WDS reporting to one place allowing the VA to use manpower properly, allow them to also have better eyes on the WDS, and also allow them to have Enterprise solutions to allow upper management to see what is going on through the SaaS solution. This solution will also allow the VA Police to send out reports or alerts to Upper management and VA Police so they can mobilize and report to where the issues are quicker, efficiently, and effectively. This will also allow the VA Police to lock down facilities easier than before where that was not possible without going to said doors and locking them physically. The WDS can do that for the VA Police now.

The key benefits are having a fully modernized weapons detection system to help the VA Police better detect unauthorized weapons entering and exiting the VA Facilities allowing the VA Police to do their job more efficiently and effectively. This will also all the VA Police to use their manpower in proper means instead of wasting man hours and money.
12_outputs: Xtract One View is equipped with advanced analytical tools that process vast amounts of data to detect unusual patterns and potential threats before they escalate. Using machine learning algorithms, it continuously improves its concealed weapons detection capabilities, adapting to new types of risks and optimizing security protocols accordingly.
16_dev_stage: Acquisition and/or Development
17_impact_type: Both
18_date_initiated: 9/23/2024
23_contract_piids: Contract has not been awarded yet.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
31_data_catalog: No
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.
35_demo_features: Other
35_question_type: Unknown
37_custom_code: Yes
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: No
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 weekly monitoring of any harms resulting from the use of newly implemented electronic health record system – FEHR across VA
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: This AI based monitoring system will be used to inform and improve the EHR system and it's functionalities to prevent and mitigate any patient safety events.
12_outputs: Classification of harm categories from EHR
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 10/3/2022
22_dev_method: Developed in-house.
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
28_iqa_compliance: Not Applicable
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
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: Less than 6 months
43_infra_provisioned: No
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: No documentation is available: No documentation detailing model performance metrics, model architecture, features and intended use of the models have been created or are currently accessible to other data science teams within the agency.
2_use_case_name: Podimetrics
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: Our involvement with Podimetrics is limited to receiving and processing escalation reports generated by their SmartMat system. Podimetrics’ platform uses its proprietary technology, to generate these reports. However, our role does not involve developing, deploying, or controlling  AI systems. Instead, we serve as a conduit, ensuring that these escalation reports are shared with the appropriate clinicians to facilitate timely and informed care for Veterans.
12_outputs: Our interaction with the Podimetrics platform is strictly limited to data transfer and integration of the generated reports. We do not engage with their proprietary technology, or decision-making processes inherent in the Podimetrics platform. Our role is supportive and integration-focused, solely aimed at ensuring seamless communication and clinical follow-up based on the provided reports.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 12/1/2019
19_date_acq_dev_began: 12/1/2019
20_date_implemented: 12/1/2019
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: No
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: No
38_code_access: No – agency does not have access to source code.
43_infra_provisioned: Other
43_question_type: 
N/A

45_compute_request: Other
45_question_type: 
N/A

51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Thematic Analysis of Chief Resident in Quality and Patient Safety (CRQS) Capstone Projects Using BERT Modeling
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 intended purpose of the AI is to perform a detailed thematic analysis of CRQS capstone projects to identify key themes and trends, with the expected benefit of enhancing the national curriculum for quality and patient safety in healthcare.
12_outputs: The AI system's outputs will be categorized themes, patterns, and trends derived from the analysis of CRQS capstone projects, which will inform curriculum development and improvement strategies.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/1/2024
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: The model used in this use case will be trained, fine-tuned, and evaluated using agency-owned data comprising Chief Resident in Quality and Patient Safety (CRQS) capstone project documents, which detail various quality improvement initiatives and patient safety measures undertaken at VA Medical Centers.
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: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
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: My HealtheVet VSignals Main Improvement Summary
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: My HealtheVet is the VHA patient portal to access to refill medications, secure message with care teams, access medical records and view appointment information.  To ensure Veterans are being provided the best patient portal experiences, My HealtheVet collects many surveys (over 26k a month).  AI provides initial summarizing and grouping functions for open-text questions to identify trending issues and new features requested.  Rather than having humans read each comment and manually sorting, AI enhances trend identification and resource prioritization.
12_outputs: Veteran feedback is stripped of PII and then sorts comments based on product area, summary is primarily based on how often key words are mentioned.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/19/2022
19_date_acq_dev_began: 9/6/2022
20_date_implemented: 9/9/2022
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 36C10X20D0007
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: This is using open-sourced code requiring no training, essentially performance was evaluated if correct keys words were logged to provide useable outputs.
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: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Other
45_question_type: BISL did not consider the Python script AI, as it is more of a key word identification program.  No resources were required for model training and development.
47_timely_resources: Other
47_question_type: None required.
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.
2_use_case_name: ESD-Speech Sentiment and Analytics
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: Transcribing and summarizing a recorded meeting or interview using AI.
11_purpose_benefits: Utilizing AI to analyze speech sentiment of internal customers (non-veterans) to improve quality of service at the Enterprise Service Desk.
12_outputs: Various reports and dashboards for ESD leadership and the Tier 1 vendor partner to assess quality of service.  The AI component makes it possible to assess and analyze all contacts with the ESD as opposed to the ~2% of contacts we can assess today using manual approaches.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 3/30/2024
2_use_case_name: Sentiment analysis for app feedback
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: Functional Overview: The objective is to perform sentiment analysis on text reviews to classify their content as positive, negative, or neutral, and to provide a processed dataset for further downstream analytics.
 
System Overview: The system uses a trained model, derived from an open-source model and fine-tuned for the specific dataset, to analyze text reviews from various sources, including external sources (Google and Apple stores) and internal sources (FeedbackUI and VA Mobile). The model classifies reviews as positive, negative, or neutral.
 
Data: The data sources include:
 
Text reviews from the VA's mobile applications on the Google and Apple stores (external sources)

Reviews from FeedbackUI (internal source, available in the OIA_MobileHealth database)

Reviews from VA Mobile (internal source, available via CSV files on the Mobile VA's internal website)

Users: The users of this system are likely the OCC Data Science Team, who are responsible for developing and maintaining the pipeline.
 
Target Audience: The target audience OCC mobile application developers and internal stakeholders who may be interested in analyzing and understanding the sentiment and feedback from these reviews.
 
12_outputs: perform sentiment analysis on text reviews to classify their content as positive, negative, or neutral, and to provide a processed dataset for further downstream analytics.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 12/21/2022
19_date_acq_dev_began: 3/8/2024
20_date_implemented: 4/24/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: Unable to identify in time, working with contracting team to close
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
33_agency_data: CDW - VA application text review commentary for sentiment analysis
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.
35_demo_features: Other
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: SDP / HDAP - working with contract team to identify most applicable ATO system name
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: ESD-Predictive Intelligence
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: OIT: Office of Information & Technology
8_topic_area: Mission-Enabling (internal agency support)
10_commercial_ai: None of the above.
11_purpose_benefits: The intended purpose is to leverage Machine Learning on existing ticketing data supporting internal customers (non-veterans) to reduce mean time to resolution for internal customers while also increasing ticket routing accuracy and identifying major incidents more quickly as they occur.
12_outputs: Outputs involve pre-populated assignment groups in tickets within the ServiceNow IT Service Management platform to provide more accurate ticket routing and dashboard information for Incident Management staff to review highlighting potential major incidents beginning to occur in the environment.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 7/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Ticketing data in the ServiceNow IT Service Management platform.
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_demo_features: Other
35_question_type: "None" was not an option, but the answer here is none.
37_custom_code: No
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: ServiceNow 
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Other
43_question_type: It's part of the ServiceNow IT Service Management platform
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: AI Health Coach
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: The Health Coach and AI components are different. The Health Coach helps improve adherence by sending evidence based nudges (this doesn't involve AI). The AI makes the health coach more effective by adjusting the number of nudges sent to the engagement of the Veteran.
12_outputs: The Health Coach has a pre-selected decision tree (ex: did you miss your medication -> if yes, how many medications did you miss -> did you miss them because you ran out of refills). The AI simply helps determine how often to send those nudges based on response rates.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 5/1/2024
19_date_acq_dev_began: 9/22/2022
20_date_implemented: 5/1/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: NG15SD26B
24_hisp_support: No
27_public_info: No
28_iqa_compliance: The VHA has worked with the NAII, ISSO, Privacy, and VA Innovation Unit (OIT) through Angela Gant Curtis and coordinated with AI leads on efforts such as Kimberly McManus. After rigorous privacy, security, and safety review by clinicians in addition to the creation of standard operating procedures the AI Health Coach was put into place for Veterans who were matched based on agency data in a closed server. Information is not disseminated to the public via the AI Health Coach but only provided to Veterans actively engaged in primary care.
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Master Patient Index (IEN, DOB, Address, Phone, PCMM)
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: (IEN, DOB, Address, Phone, PCMM)
37_custom_code: No
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: 1366
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Yes
45_compute_request: Other
45_question_type: We work directly with the contractor for model details. We assess efficacy based on clinical outcomes at this time, given that the overall program will be run for a total of one year.
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.
2_use_case_name: Verathon Prime Plus
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: It's a bladder scanner.  The AI component is used in relation with detection of bladder.  It's aim is to increase accuracy.
12_outputs: It outputs assisted measurements related to bladder size and shape.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 1/1/2024
19_date_acq_dev_began: 1/1/2018
20_date_implemented: 1/1/2018
22_dev_method: Developed with both contracting and in-house resources.
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
35_demo_features: Other
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: No
45_compute_request: No
47_timely_resources: No
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.
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: App Feedback categorization 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: Functional Overview: The objective is to categorize review texts into specific categories to provide more granularity and enable users to identify trends and perform analytics on the data.
 
System Overview: The system uses a trained open-source model that has been fine-tuned on the app feedback data to categorize review texts into specific categories, including Tech, Usability, Content, Idea, and Other.
 
Data: The data sources include:
 
Text reviews from the VA's mobile applications on the Google and Apple stores (external sources)

Reviews from FeedbackUI (internal source, available in the OIA_MobileHealth database)

Reviews from VA Mobile (internal source, available via CSV files on the Mobile VA's internal website)

Users: The users of this system are likely the OCC Data Science Team, who are responsible for developing and maintaining the pipeline.
 
12_outputs: The objective is to categorize review texts into specific categories to provide more granularity and enable users to identify trends and perform analytics on the data.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 12/2/2023
19_date_acq_dev_began: 3/1/2024
20_date_implemented: 4/16/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: Unable to identify, working with contracting team to find most applicable PIID
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
33_agency_data: CDW - VA application feedback
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 – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: SDP / HDAP - working with contract team to better identify applicable system name
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.