GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: CareCentra Next Level Personalized 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: This is an evidence based, randomized control trialed AI solution that focuses on precision nudging.  It is intended to improve quality of care and reduce view alerts.  Its focus is on identifying personalized behavioral change algorithms for each veteran to identify timing, channel, 
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 1/1/2024
19_date_acq_dev_began: 1/1/2024
20_date_implemented: 5/1/2024
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: CRADA: Cooperative research and development agreement
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: Other
31_question_type: not applicable
33_agency_data: not applicable
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: Race/Ethnicity, Sex/Gender, Age, Socioeconomic Status, Residency Status
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: Arches
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Other
45_question_type: not applicable
47_timely_resources: Other
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.
2_use_case_name: SafePointe WDS - OSSO
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Law & Justice
10_commercial_ai: Creating visual representations of data sets for reports and presentations using AI.
11_purpose_benefits: Heat map detection for weapon detection software.
12_outputs: Areas of interest for a heat map that include objects that would produce heat indicators aligned with heat signatures of known types of different weapons.
16_dev_stage: Retired
17_impact_type: Both
18_date_initiated: 6/23/2023
19_date_acq_dev_began: 6/27/2022
20_date_implemented: 2/1/2024
21_date_retired: 5/1/2024
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: Yes
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: 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
45_compute_request: Other
47_timely_resources: Other
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: GE Xeleris 
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: Xeleris V leverages AI to accurately segment organs for quantitation and dosimetry calculations. Q.Thera AI demonstrated an average reduction of 58% in the time required for the user to process and calculate dose3.
12_outputs: Provide patient dosimetry calculations.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
40_has_ato: Yes
41_system_name: MD-LITE ID: 2278
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Genesys Cloud Contact Center
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: Assist staff with knowledge based recommendations, conversation summarization and quality assurance metrics automation. This will allow the staff to react quicker to customer needs and spend less time researching for information or performing repetitive tasks and instead focus more on customer interactions.
12_outputs: Most of genesys outputs are surfacing knowledge to assist the staff with customers interactions or automating repetitive tasks such as scoring quality forms for those interactions or routing of customers upon discovered intents.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 2/22/2024
19_date_acq_dev_began: 7/26/2024
2_use_case_name: Verkada Camera - OSSO
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Law & Justice
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Video search function inside a physical security camera solution.
12_outputs: Video and images from the AI searches inside the physical security camera solutions.
16_dev_stage: Acquisition and/or Development
17_impact_type: Both
18_date_initiated: 5/1/2024
19_date_acq_dev_began: 9/1/2024
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
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: 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
45_compute_request: Other
47_timely_resources: Other
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: MRI - GE Signa Artist
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: AIR™ Recon DL is a deep-learning-based reconstruction algorithm that enables radiologists to achieve pin-sharp images quicker. By removing noise and ringing from raw images, scans are consistently crisp. Improved signal-to-noise ratio (SNR) means scan times are cut by up to 50%, smoothing workflow and enhancing the patient experience
12_outputs: MRI image reconstruction.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
40_has_ato: Yes
41_system_name: MD-LITE ID: 2278
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Google Cloud Platform - CCAI / Dialogflow 
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: OIT: Office of Information & Technology
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: None of the above.
11_purpose_benefits: to allow for better self service options to veterans to include virtual voice/chat agents
12_outputs: the output of GCP voice/chat is text to speech (TTS) and speech to text (STT)
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/1/2022
19_date_acq_dev_began: 12/1/2022
2_use_case_name: Axon Body Camera and DMS - OSSO
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Law & Justice
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: To be able to search the data repository with better results, better redaction abilities for certain capabilities and on the Fleet System only to have better license plate recognition. 
12_outputs: To have better outputs in searching the systems data repository for case files, evidence locker, and DMS; to have a better result when doing the redaction of evidence, files, videos and photos; and on the Fleet system only to have better results in identifying licenses plates in the licenses plate recognition part of the system.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 9/1/2020
19_date_acq_dev_began: 9/26/2022
20_date_implemented: 1/3/2023
22_dev_method: Developed with contracting resources.
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: Yes
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.
40_has_ato: Yes
41_system_name: Axon
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
45_compute_request: Other
47_timely_resources: Other
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: Ultrasound - GE Logic E10
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: Auto Preset Assistant:
Automatically activates the correct preset for the anatomy being scanned

Auto Abdominal Color Assistant:
Detects which abdominal organ is being scanned, automatically switching to the optimal color flow parameters

Auto Renal Measure Assistant:
Automatically detects the kidney and measures the length, height, and width
12_outputs: Clinician decision support and workflow error reduction.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
40_has_ato: Yes
41_system_name: MD-LITE ID: 2278
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Radiology - Siemens YSIO Max
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: Bone supression: Supressess bones on digital images without the need for two exposures. Diamond View: improves image contrast and detail to improve image quality.
12_outputs: Increase radiology digital image quality.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
40_has_ato: Yes
41_system_name: MD-LITE ID: 2278
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Enterprise Precision Scanning and Indexing (EPSI) Next: Enhancing Healthcare Services for Veterans
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Health & Medical
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: Generative AI required for summarization and note creation.
12_outputs: The output generates a summary of the patient health record for the EPSI product indexer workflow.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 6/2/2021
19_date_acq_dev_began: 8/2/2023
2_use_case_name: Parable 3D Wound Care Management System
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: Traditional wound care methods call for hand measurements to be taken of the patients wound, increasing imprecision that comes with manual measurements, along with the increased contact with the wound and potential for infection. Parable 3D Wound Care Management System aims to automate the process of wound care by using traditional computer vision based volumetric measurements to capture images of the wound and create 3D polygon mesh representation of said wound. The main benefit of the system is the automated, contactless measurements that will reduce vectors for infection and alleviate the difficulty, imprecision, and variability of manual measurements. This allows the clinicians to spend more time on patient care, analyzing wounds, and encourage proper healing for the patient.
12_outputs: 2D and 3D volumetric measurements are computed based on the segmented 3D reconstruction to produce length, width, surface area, depth, and volume measurements.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
40_has_ato: Yes
41_system_name: Parable ID: 665
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: VEO Virtual Analyst Proof of Concept
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: OIT: Office of Information & Technology
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: The AIDV Dev/Test environment will provide a proof of concept to provide generative AI capabilities in CX Insights and SDP. This will show capability to allow users to intuitively and easily work with dummy data and provide a sandbox to develop generative AI tools. The development of Virtual Analyst tool will provide value to the veteran by using generative AI to predict and improve services to the veteran.
12_outputs: Outputs will include data-informed English-language responses to English-language questions asked about VEO machine learning models and customer experience metrics. These outputs will be informed by dummy versions of these data.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 2/20/2024
19_date_acq_dev_began: 4/25/2024
2_use_case_name: GE Portable Critical Care Suite 2.x
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/K183182.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: Patient Care Systems Integration Program (PCSIP)
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: The PCSIP AI is intended to improve the access to care for Veterans by increasing capacity of VAMCs up to 20% without adding more staff or resources. 
The expected benefits of the AI are listed below. 
(1) Between 20% to 50% reduction in charting time. 
(2) Reduced cancellations/delays/no-shows between 5% and 40% depending on type of appointment of patient. 
(3) Between 5% and 20% increase in number of scheduled cases in surgery, primary care, radiology, behavioral health, and specialty care.
(4) Between 5% and 20% improvement in Door-to-Doc times and Length of Stay in Emergency Department.
(5) Between 5% and 20% improvement in patient flow between ED and Wards; and between Surgery and Wards.

12_outputs: All outputs listed below are written to patient record in Vista/CPRS and Cerner after review by care providers and electronic signature by them. 1. Ambient Dictation and AI generates,       a. ED Triage Note,        b. ED Evaluation Note,        c. Columbia SSRS,        d. After Visit Summary,        e. Follow up appointment note,        f. Patient Letter       g. Care Pathway Mapping for generating list of diagnostic orders, medication orders etc. 2. Radiology        a. Resulting Note in MRI, CT, Ultrasound, X-Ray, and others.       b. Protocoling by Radiologist and Technologist notes written to Vista  3. Surgery       a. Scheduling optimization       b. Preop readiness checks and reports       c. Preop Instruction Compliance       d. Discharge Instruction Compliance       e. ERAS protocol compliance 4. Behavioral Health       a. Generation of Columbia-SSRS and Comprehensive SSRS for suicide reduction       b. Video monitoring of mental health inpatients 5. Primary Care       a. Ambient Dictation driven note generation.       b. Outside records consolidation for Patient Summary extracted from faxes. 6. Instant availability of all the relevant patient information at the point of care based on schedule of the patient and location of the patient and care provider at the point of care.
16_dev_stage: Implementation and Assessment
17_impact_type: Both
40_has_ato: Yes
41_system_name: PCSIP ID: 1258
51_extension_request: Yes – Agency requested an extension for this use case.
2_use_case_name: Billing Claims Prediction
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 intended purpose of the AI is to support Revenue Operations Billing activities by providing a propensity to bill flag based on previous user activity in the Revenue Operations Workflow Tool. The output of the AI is strictly intended to be used for decision support for users who are determining whether an encounter is billable or not. 
12_outputs: The model was trained on Third Party Community Care Billing and Facility Revenue Activity Codes 10, 30, 31, 32, 33, 34, 35 applied from 2021-2023. Initial run at Florida Caribbean Consolidated Patient Account Center (FCCPAC) was trained on 1.2 Million accounts. By training the model on previous activity data, an account can be classified as likely to have a billable or non-billable outcome.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 2/1/2024
19_date_acq_dev_began: 2/1/2024
20_date_implemented: 6/14/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: VA118-16-D-1006 36C10B22N10060040
24_hisp_support: No
27_public_info: No
28_iqa_compliance: N/A.
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: 1.2 million claims trained from Community Care Claims Data from  Community Care Reimbursement System (CCRS), Electronic Claims Adjudication Management System (eCAMS) and VACS sourced via Central Data Warehouse (CDW).
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: Yes
41_system_name: Revenue Operations Workflow Tool - RPA
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: 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: ScienceLogic Artificial Intelligence Operations (AIOPS) Software Subscriptions with Maintenance and Professional Support Services.
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: OIT: Office of Information & Technology
8_topic_area: Other
8_question_type: Cloud
10_commercial_ai: None of the above.
11_purpose_benefits: Performance monitor of cloud applications 
12_outputs: The SL1 AIOps solution enables VA to  diagnose reasons for outages and rapid capability to respond, resolve and prevent such  outages across the full domain.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/24/2020
19_date_acq_dev_began: 9/24/2020
20_date_implemented: 9/24/2020
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: NNG15SC91B/36C10B20F0417
24_hisp_support: No
27_public_info: No
28_iqa_compliance: N/a -
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: No
33_agency_data: N/a
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 - Monitor applications in the cloud
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: VA Enterprise Cloud 
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Other
43_question_type: For the VA's cloud
45_compute_request: Other
45_question_type: For the cloud
47_timely_resources: Other
47_question_type: Cloud
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.