We strengthen public health systems by closing care loops for people with high-risk conditions.
We work inside government health systems in India to track people at risk and make sure care reaches them.
Our approach
We build one path from data to decisions. Population-level data shows who is at risk. Frontline workers act on it. Health officials use it to prioritize where money and people go. And the whole system learns from what worked and what did not.
This approach runs across every program area we support, from family planning and maternal health to child health, immunizations, and severe and moderate acute malnutrition.
Our theory of change
When community health workers have digital tools that capture longitudinal data, that data becomes risk-based insight — surfacing exactly who needs attention, and when. Health Action Centers close the loop on that insight, turning it into timely, tracked care.
The result: high-risk patients who would otherwise be missed get reached, at population scale. Governments own and sustain the model. And the public health system itself gets better at its job over time.
We measure impact on three levels
We measure impact on three levels:
The Person
Care loops closed
60,007,027
Population digitally tracked
62,8
Referrals initiated
62
High-risk population referred to care pathways
62,000
High-risk population completing the loop of care
The Provider
Hours returned to care
62,848
CHWs reached
32%
Weekly active frontline health workers
62,848
CHWs using KB platforms for program activity
602
Hours returned to care
602
Data-driven decisions made
602
Meaningful coordination
The System
Decisions made on data
602
Health officials onboarded on data-use platforms
620
Data-based decisions made, cumulative
$20,500,000
Government co-financing unlocked
The Person
Care loops closed
People’s health tracked
60M
Referral loops closed
10K
Figures are cumulative across programs (family planning, maternal health, child health, immunizations, SAM/MAM).
The Provider
Hours returned to care
Data-driven CHWs
62.8K
Community health teams enabled
Hours returned to care
--
The System
Decisions made on data
Data-driven health officials
600
Data-driven decisions
100+
Government co-financing
$20.5M
What the evidence shows
The Person
+12%
improvement in full infant immunization in a randomized controlled trial. Targeted follow-up with the right families raised full immunization rates.
The Provider
30 days → 4 hrs
data response time, after the move from paper to CHIP. When data reaches the district in hours instead of a month, it gets used in review meetings while it still matters
The System
8×
improvement in the TB presumptive case-detection rate in Rajasthan. Screening the most vulnerable people first, guided by vulnerability targeting, found far more TB cases than routine screening
What we've learned
01​
Scaling a digital health platform depends on the incentives and system design around it, not the tool alone.
02​
Data culture changes when data reaches the district in hours. It gets used in review meetings, and government has financed that speed at state scale.
03
Targeted follow-up moves outcomes. It raised full immunization in an RCT, and screening the most vulnerable first produced an 8-fold gain in TB case detection.
04​
Technology alone does not change a health system.
05​
We paused an LLM-based assistant for ASHAs from clinical use after a 12-month study, when reliability plateaued and the safety layer needed more work. Null results shape our roadmap.
The modeled social return of our Health Action Centers.
₹6.60
returned per ₹1 invested (modeled; range 0.6–41 depending on assumptions)
In 2026 we modeled the social return of our two Health Action Centers from a register of 1,320 documented cases. The central estimate is ₹6.60 of health value per ₹1 invested (Udaipur ₹7.0 over its two-year corpus; Nandurbar ₹4.5 in its first 8 months), at roughly $700–850 per modeled DALY averted. The ratio depends on the attribution share and the value assigned to a healthy year of life; external evaluation of both centers is planned. The full assumptions and methodology are available on request.
$700–850
per modeled DALY averted
External evaluation planned
SROI validation RFPs are open for both Health Action Centers
From visibility to closed loops.
We have built visibility into who is at risk. The next step is closing the referral loop, so that the people we can now see are followed all the way to care. Closing a loop takes more than a referral. It takes reminders, follow-up visits, and coordination between workers until care is confirmed. That work is where we are putting our attention next.
Our 2025–2030 strategy is to move from digital adoption to government-owned data-to-action pathways. We are working through four questions:
01​
How can digital health systems generate actionable insights, not just data?
02​
What policies, governance, and operational practices help governments adopt and sustain data-driven decision-making?
03
Which implementation models best ensure high-risk individuals are identified, referred, and followed up on time?
04​
How can emerging tools like AI, predictive analytics, and vulnerability mapping strengthen public health planning and resource allocation?
