2_use_case_name: Smart AI Bot Assistant
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: A machine learning and natural language processing algorithm will be trained to provide real-time prompts of resources to improve the quality of crisis calls for the Veterans Crisis Line (VCL). Curated, vetted resources will be prompted by keywords for responders to consider during a crisis call. Natural language processing will be used to listen and document during and after the call, organizing the documentation to include topic areas and key points. This will decrease the time spent on documentation, improve the responder workflow, and provide needed downtime for responders to debrief with colleagues and take breaks. Additionally, the chat bot could detect the need for supervisory support during a call, improving real-time communication between a VCL responder and their supervisor. The use cases for the AI will be reviewed by VA experts and subject matter experts, with the draft use cases requiring technical steps to build, pilot test, and refine.
12_outputs: Smart Responder Chat Bot Use Case #1 – Real time resources for VCL responders   Using machine learning and natural language processing an algorithm will be trained based on feedback from the Veterans Crisis Line staff and crisis line responders to provide real-time prompts of resources to improve the quality of the crisis call. Curated, vetted resources will be prompted by key words for the responder to consider using during a crisis call.   Smart Responder Chat Bot Use Case #2 – AI documentation for VCL responders   To improve the time each Veterans Crisis Line responder spends on documentation during and after a crisis call, natural language processing will be used to listen and document during and after the call. The documentation will be organized to include the topic areas and key points required in documentation and provide a final draft editable by the responder. This use case will; decrease the time spent by responders on documentation, improve the responder workflow for VCL operations, and provide needed downtime away from both answering calls and documentation for responder to debrief with colleagues, accomplish other tasks and take necessary breaks to improve overall responder wellbeing.   Smart Responder Chat Bot Use Case #3 – AI for supervisory support during a call   Using machine learning and natural language processing including sentiment analysis, a chat bot could detect the need for a responder to seek supervisory support. Improving the real-time communication between a VCL responder and their supervisor during a call that is challenging (eg. Inappropriate language, escalating rapidly, etc.) could be a useful use case.
16_dev_stage: Initiated
17_impact_type: Rights-Impacting

18_date_initiated: 9/19/2024
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: Other
31_question_type: TBD
33_agency_data: TBD
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: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: 6-12 months
43_infra_provisioned: Other
43_question_type: TBD
45_compute_request: Other
45_question_type: TBD
47_timely_resources: Other
47_question_type: TBD
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: 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.