
India
Healthcare
Implementing Organisation
Khushi Baby
India, Rajasthan, Udaipur
Implementing Point of Contact
Saket Kumar
R&D Lead
Contributor of the Impact Story
Khushi Baby
Year of implementation
2024
Problem statement
Affecting an estimated 12 million pregnant women annually in India, maternal anemia is a major contributor to adverse maternal and neonatal outcomes, yet screening at the last mile is constrained by invasive, consumable-dependent, or unreliable tools. Moderate anemia often goes undetected due to limited lab access and constraints with point-of-care devices, resulting in missed opportunities for early intervention and increased downstream health and system costs. MAHILA addresses this gap by enabling frontline workers to perform frequent, non-invasive screening using a smartphone camera, flagging higher-risk women for confirmatory testing and treatment while reducing unnecessary referrals.
Impact story details
Khushi Baby (KB) is a non-profit organization working as a systems enabler to public health departments in India. Established in 2016, KB-s team of 130 members include expertise from public health, product design, engineering, field implementation, and data science. Co-created with 250,000 hours of community health workers engagement, KB-s solutions have been used to track the health of over 50M beneficiaries across Rajasthan, Maharashtra, and Karnataka. Through enabling high quality data, timely insights, and effective actions, KB aims to close the loop for the public health system at the last mile.
AI Technology Used
Key Outcomes
Accuracy
Quality Improvement Access
Reach Efficiency
Productivity Resource Efficiency Inclusion
Equity
Impact Metrics
Implementation Context
India - Rajasthan, Maharashtra, Karnataka
Current study scale: >3000 pregnant women Target scale in phase 2: >30000 participants
Key Partnerships
Government: State health departments (Rajasthan, Maharashtra, Karnataka) Academia/Clinical: Kasturba Medical College (KMC), Mangalore District ecosystem partners (as applicable): Nandurbar District Administration / local health system partners
Replicability & Adaptation
Calibrate for local device types, lighting conditions, and population diversity; maintain ongoing bias monitoring across subgroups. Ensure a clear pathway for confirmatory testing and treatment so that AI screening supports-rather than replaces-clinical decision-making.
Supporting Materials
* The data presented is self-reported by the respective organisations. Readers should consult the original sources for further details.