2_use_case_name: Prediction of risk for Loss to Follow Up (LFU) among TB patients using AI
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/India
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: The AI predicts the likelihood of adverse treatment outcomes in TB patients, such as treatment failure or mortality. By identifying high-risk patients early, It facilitates timely interventions, improves patient outcomes, optimizes resource allocation, and enhances the effectiveness of TB treatment programs.
12_outputs: AI model outputs a risk score for each patient that is converted to a HIGH-RISK or LOW-RISK label after comparing it with a district-wise threshold. This helps prioritize patients for timely interventions, improving treatment outcomes and reducing the risk of adverse events
16_dev_stage: Operation and Maintenance
17_impact_type: Both
18_date_initiated: 6/1/2020
19_date_acq_dev_began: 7/1/2020
20_date_implemented: 3/1/2023
22_dev_method: Developed with contracting resources.
23_contract_piids:  72038620CA00006
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 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: Age, Gender, Other
35_question_type: Location, Height, Weight
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: Other
47_question_type: No relevant resources
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: No – Agency did not request an extension for this use case.
52_impact_assessment: No
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: - Trust: There are two issues related to trust: a. the possibility of trusting model outputs over human judgement could lead to missing of some high risk cases b. Model interpretability poses a challenge, as the algorithms may hinder healthcare professionals' ability to trust or validate predictions. 
- Accuracy: The solution may produce inaccurate or inequitable predictions due to incomplete or unrepresentative data. This could lead to underperformance on certain sensitive cohorts, further affecting trust in AI-generated outcomes.
- These risks were identified and addressed through model evaluation, regular internal data audits and routine monitoring of pilot deployments.
56_monitor_postdeploy: Established Process of Machine Learning Operations: Alongside automated testing and drift detection, model re-training and re-deployments are supported by continuous integration pipelines that are managed by machine learning and data engineers on the platform, adapting work done by data science team into repeatable scripts for re-training and re-testing a model once deployed.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: N/A - individuals are not interacting with the AI for this use case