2_use_case_name: USING PREDICTIVE ANALYTICS TO IMPROVE CARE
3_agency: United States Agency for International Development
3_abr: USAID
4_bureau: USAID/Bureau for Inclusive Growth, Partnerships, and Innovation
8_topic_area: Health & Medical
10_commercial_ai: None of the above.
11_purpose_benefits: 1) identify high-risk patients for targeted intervention - in this specific use case it's children who are at risk of malnutrition but it is meant to eventually be applicable in other settings beyond health, 2) identify frontline health workers / caregivers in need of additional follow up and support from a community health facilitator (in this project it's a community facilitator but outside of this project it would likely be a supervisor in the future)
16_dev_stage: Implementation and Assessment
17_impact_type: Both
18_date_initiated: 7/11/1905
19_date_acq_dev_began: 7/1/2020
20_date_implemented: 10/1/2024
22_dev_method: Developed with contracting resources.
23_contract_piids: 7200AA20FA00023
24_hisp_support: No
27_public_info: No
29_contains_pii: No
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.
35_demo_features: Age, Gender, Disability Status, Other
35_question_type: Caste, religion, parents' education level
37_custom_code: Yes
38_code_access: Yes – source code is publicly available.
39_code_link: https://github.com/dimagi/risk-scores
40_has_ato: No
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: 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: Planned or in-progress.
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: For this particular use case - nutrition outcome monitoring and prediction, one of the key challenges is stable and continued availability of children and their caregivers to enable continuous tracking of changes in their anthropometric measures. From a technical standpoint, the key risk for using AI in any targeted health intervention is the risk that it negatively impacts the baseline intervention (i.e. redirects resources in a way that negatively impacts the program's impact). This risk is avoided by strictly supplementing implementing partners' existing operations.
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.
62_disparity_mitigation: For this study, Dimagi has built a predictive algorithm to generate scores to identify those at highest risk for worsening malnutrition outcomes in order to target an additional intervention. The algorithm is run once at the beginning of the study to identify those to enroll in the top-up intervention, the efficacy of the model is being monitored and evaluated via an RCT.