How a Public Health Service Program Reduced Maternal and Infant Mortality Using Lumenore AI-Powered Analytics
Results Achieved:
High-Risk
Pregnancy Recall
67%
recall rate (33,500of 50,000 flagged)
Maternal Mortality
22%
reductionTribal Region
Maternal Mortality
30%
reductionImmunization Coverage
18%
overall increaseTribal Immunization Rates
25%
increase (80,000 children)
Customer Overview:
They are a government-led public health service entity focused on strengthening healthcare delivery across urban, rural, and tribal regions. The program integrates maternal and child health, immunization, disease control, and health infrastructure initiatives, with a mandate to deliver accessible, affordable, and equitable healthcare, especially for vulnerable populations.
Business Challenge:
The program faced persistent challenges in reducing maternal and infant mortality, particularly in underserved and rural regions.
High maternal and infant mortality rates, driven by limited access to timely care.
Scattered data across multiple systems,
preventing a unified view of program effectiveness.
Public health officials lacked the tools to translate their program data into actionable insights for improving healthcare services
Geographic and socio-economic disparities leading to uneven resource allocation.
Rapidly changing population health needs due to migration, seasonal diseases, and socio-economic shifts.
Why the Problem Mattered:
For public health programs operating at population scale:
- Delayed insights translate directly into preventable deaths.
- Biases in data can exclude vulnerable communities.
- Reactive planning limits the effectiveness of national and state-level interventions.
- Program success depends on timely identification of high-risk cases and targeted action.
The Lumenore Solution:
Lumenore implemented a large-scale, AI-powered public health analytics platform designed to support evidence-based decision-making.
Centralized & Cleaned Program Data
Integrated antenatal care, institutional delivery, immunization, socio-economic, and geographic data into a single, error-free analytics environment.
Interactive Dashboards Across Programs
Delivered dashboards tracking 22 national and 13 state-level health programs, enabling visibility into performance, gaps, and outcomes.
AI/ML Models for Risk Identification
Implemented Ai models to:
- Identify high-risk pregnancies
- Prioritize interventions
- Improve maternal and infant care delivery
Bias-Aware Modeling Approach
Addressed caste (ethnicity) and socio-economic bias by embedding contextual variables and validating models with local SMEs to ensure equitable representation.
Secure, Role-Based Data Access
Ensured compliance with national data regulations through controlled access and governance.
Customer Speaks:
Earlier, our teams were working with fragmented data and limited visibility into which populations needed immediate attention. With Lumenore, we now have a unified, real-time view of maternal and child health risks across regions. This has helped us identify high-risk pregnancies sooner, prioritize interventions in underserved areas, and measurably improve health outcomes at scale.
Why They Chose Lumenore:
- Ability to handle large-scale public health data (2M+ records)
- Advanced AI/ML expertise with bias-aware modeling
- Secure, compliant analytics platform
- Proven success in outcome-driven public health initiatives
- Scalable dashboards for national and state programs
How Lumenore Can Support Your Use Case:
Public health organizations can use Lumenore to:
- Identify high-risk populations early
- Monitor national and regional health programs in real time
- Reduce disparities across rural and tribal regions
- Improve immunization and maternal health outcomes
- Enable data-driven policy and resource allocation
Lumenore is built for high-velocity data, multi-platform ecosystems, and global-scale competitive environments.
