2_use_case_name: VA CART Percutaneous Coronary Intervention Bleeding Risk 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: The VA CART bleeding risk model is based on logistic regression using baseline clinical and procedural variables to predict post-PCI in-hospital bleeding events. The model is available to facilitate risk stratification at the point of care.
12_outputs: Post Percutaneous Coronary Intervention bleed prior to hospital discharge
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 1/1/2020
19_date_acq_dev_began: 6/5/2020
20_date_implemented: 7/1/2021
22_dev_method: Developed in-house.
24_hisp_support: Yes
25_hisp_name: Veterans Health Administration
27_public_info: No
29_contains_pii: No
30_saop_review: Yes
31_data_catalog: Yes
33_agency_data: Patient characteristics (demographics, comorbidities and prior procedures, procedural characteristics, labs, medication history, and  Post-PCI bleed prior to hospital discharge.
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: Height, Weight, clinical characteristics: PCI status, indications, cardiogenic shock, PAD, AFIB, CKD, lab values: hemoglobin, INR, Troponin
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: Clinical Assessment, Reporting and Tracking Application (VASI ID 1079, EMAS ID 1808) 
42_dev_tools_wait: 6-12 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.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Yes
53_real_world_testing: Performance evaluation in operational environment: The AI use case has been tested in an operational environment before being fully implemented as a solution, or has been tested in simulated or controlled environment using operational or synthetic data.
54_key_risks: Risk: Drift.  The model is reassessed periodically.
55_independent_eval: TRUE
56_monitor_postdeploy: Intermittent and Manually Updated: A plan for monitoring the AI use case is in place, and requires data science teams to work with DevOps engineers to manually update models at scheduled intervals, and create metrics to detect data distribution shifts between the operational environment and the training data for the model. 
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: Online - in the terms or instructions for the service.
61_adverse_impact: No
62_disparity_mitigation: Included in pool of candidate variables
63_stakeholder_consult: Direct user testing
65_appeal_process: No – it is not operationally practical to offer this.
66_no_appeal_reason: The model is for use by physicians as one additional clinical input. Ultimately the physician makes decisions after looking at all patient clinical information including demographics.  Physicians can offer feedback but the patient is not directly exposed to this model.
67_opt_out: Yes
67_question_type: Physicians can choose to use the model or not and their decision is always with human input.