GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: SOMATOM go.Up; SOMATOM go.Now; SOMATOM go.All; SOMATOM go.Top; SOMATOM go.Sim; SOMATOM go.Open Pro; SOMATOM X.cite; SOMATOM X.ceed
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/K233650.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: HCD User Feedback Summary, Analysis, and Design
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: The Human-Centered Design (HCD) team uses Large Language Models (LLMs) and custom agents to enhance user experience (UX) research. The primary purpose is to improve the usability, accessibility, and clarity of user feedback, providing a better understanding of the needs of Veterans and healthcare providers.
12_outputs: The AI system generates insights, such as user stories, personas, and workshop summaries, which are used to inform design decisions.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
40_has_ato: Yes
41_system_name: SDP ID: 1301
2_use_case_name: 3M/Solventum 360 Encompass Computer Assisted Coding - Auto Suggestion
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: The intended purpose is to provide medical coder end users with suggested ICD 10, CPT, and HCPCS codes to select to be assigned/associated to an outpatient, inpatient or surgery encounter based on the provider's clinical documentation.  The expected benefit for the medical coder to gain efficiency in the medical coding process that results in more accurate and timely coding of encounters.
12_outputs: Suggested ICD 10 CM, ICD 10 PCS, CPT, and HCPCPS codes to a medical coder end user based on the clinical documentation the AI system reads.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
2_use_case_name: Vivid iq
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/K221148.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: Vivid E80/ Vivid E90/ Vivid E95
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/K220882.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: EchoPAC Software Only, EchoPAC Plug-In
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/K220940.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: Using Partially Observed Markov Decision Process to Implement a Response to Intervention Framework for Veterans with Post-Traumatic Stress Disorder 
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 Partially Observed Markov Decision Process (POMDP) model will be eligible to help clinicians to provide more personalized treatment to minimize or eliminate the PTSD symptoms in a short amount of time (thus costing less) and enable veterans to return to civilian life more quickly than the current practice
16_dev_stage: Initiated
17_impact_type: Both
18_date_initiated: 12/4/2023
22_dev_method: Developed in-house.
23_contract_piids: research IRB approved
24_hisp_support: No
27_public_info: No
28_iqa_compliance: The project is required to adhere to all the safety requirements governed by IRB
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Other
31_question_type: Already identified
33_agency_data: labs, demographics, visits, problems, medications, social history
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, Socioeconomic Status, Ability Status, Residency Status, Marital Status, Employment 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: VINCI
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 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: Microsoft Power Automate and AI  
3_agency: Department of Veterans Affairs
3_abr: VA
4_bureau: VHA: Veterans Health Administration
8_topic_area: Transportation
10_commercial_ai: None of the above.
11_purpose_benefits: To retrieve information from external facilities (SNF, CNH) in order to allow our facility (Veterans Transport Program) to facilitate scheduling of transport of eligible veterans to and from our facility or to community care appointments.
12_outputs: The AI program extracts information from a request form (pdf) attached to an email. The information is then populated in an Excel spreadsheet.  The data points include the date of request, date transport needed, name of requesting facility, person completing the form, Veteran's last name, first name and last 4, attendance needed, trip type, pick up time, appointment time, facility pick up address, appt location, destination address, oxygen needed, weight of patient.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 7/5/2023
19_date_acq_dev_began: 5/1/2023
20_date_implemented: 6/1/2023
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
37_custom_code: No
40_has_ato: No
41_system_name: MS Office 365 - Power Automate 
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: Not applicable
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: IVC Access Transformation Transformative Care Modalities VA Health Connect Using Machine Learning to Evaluate First Contact Resolution (FCR)
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: For analytics purposes to generate performance metrics on First Contact Resolution for the Clinical Triage core service. Does not influence medical or critical decisions and does not use any sensitive Veteran data.
12_outputs: The output of the model allows us to classify each clinical triage as resolved or not resolved, so that we can generate performance metrics on First Contact Resolution for the Clinical Triage core service.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 10/23/2023
19_date_acq_dev_began: 10/24/2023
22_dev_method: Developed with contracting resources.
23_contract_piids: VA118-16-D-1015, 36C10B21N10150056
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: The language model is being used to compare free text fields within the Customer Relationship Management (CRM) application that capture details about the purpose of each case.
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: Summit Data Platform
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: ECG system software
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: Commercially available software to assist with electrocardiogram interpretation
12_outputs: Produces an ECG tracing and provides reading on ECG parameters
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: Avicenna ICH 
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 function of the AI is to detect possible intracranial hemorrhage (ICH) on CT head exams to aid and expedite diagnosis.
12_outputs: The input is DICOM format CT head image data from VHA facilities that submit the exam to VHA National Teleradiology Program (NTP) for interpretation.  The output is DICOM object written to the NTP Picture Archive Communication System (PACS) for review by an interpreting VHA NTP Diagnostic Radiologist.  Future output will also include a DICOM structured report to the VHA NTP PACS Powerscribe dictation software.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 4/1/2020
19_date_acq_dev_began: 6/1/2020
20_date_implemented: 10/1/2021
22_dev_method: Developed with contracting resources.
23_contract_piids: 36C10B19C0016
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: N/A - FDA 510K cleared COTS product, trained using OEM 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_demo_features: Age
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: National Teleradiology Program Next Generation Picture Archive and Communication System #2418
42_dev_tools_wait: Less than 6 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: 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: Densitas Density 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: The function of the AI is to incorporate quantitative mammographic density with the qualitative 4-category (A, B, C, D) breast density scale that aligns with the ACR BI-RADS Atlas 5th edition breast density scale.  The AI accounts for both the degree of mammographic density and the presence of localized areas of dense breast tissue to aid and expedite mammographic diagnosis.
12_outputs: The input is DICOM mammography image data from VHA facilities that submit digital mammogram exams to VHA National Teleradiology Program (NTP) for interpretation.  The output is DICOM object written to the NTP Picture Archive Communication System (PACS) for review by an interpreting VHA NTP Diagnostic Radiologist.  Future output will also include a DICOM structured report to the VHA NTP PACS Powerscribe dictation software.
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 6/1/2022
19_date_acq_dev_began: 9/1/2022
20_date_implemented: 11/1/2022
22_dev_method: Developed with contracting resources.
23_contract_piids: 36C10B22C0042
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
29_contains_pii: Yes
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: N/A - FDA 510K cleared COTS product, trained using OEM 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_demo_features: Age, Sex/Gender
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: National Teleradiology Program Next Generation Picture Archive and Communication System #2418
42_dev_tools_wait: Less than 6 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: 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: CLARUS
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/K191194.pdf).
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 2/4/2022
19_date_acq_dev_began: 2/4/2022
20_date_implemented: 2/4/2022
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.