Innovation
At ARMMAN, we are building a continuous innovation process that is human-centred, data-driven, and evidence-based. We put the women we serve, pregnant women, mothers, and health workers at the centre of our innovation approach.
Our iterative innovation process is creative and cost-effective, and lets us design for scale through evidence. We also leverage emerging technologies including Artificial Intelligence (AI) to make our programmes more engaging and effective.
Human-centred innovation
We make sure that the voice of our users shapes the design and development of our products at every step of the innovation process.
Emerging technologies
We partner with academic and research collaborators to leverage emerging technologies such as AI / machine learning and large language models to make our programmes more engaging and effective.
Data-driven approach
We use insights from how millions of users interact with our programmes and pilots to identify opportunities for innovation.
Innovation sandboxes
We are creating sandboxes that cover the urban, rural and tribal contexts to act as controlled environments for testing our pilots across the country.
Enhancing Interventions through Innovation
ARMMAN is using AI and the WhatsApp platform to increase the efficiency of our interventions. Customising messages to address the unique needs of the most vulnerable women and families, we are aligning with a fit-for-purpose approach that takes into account differential access to technology, variable health needs and socio-economic inequities.
Prediction
model using
Artificial
Intelligence
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Prediction model using Artificial Intelligence
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Using WhatsApp
as a delivery
channel for
Kilkari 2.0
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Using WhatsApp as a delivery channel for Kilkari 2.0
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High Risk
pregnancy
pilot for
Kilkari
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High Risk pregnancy pilot for Kilkari
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WhatsApp
chatbot for
health
workers
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WhatsApp chatbot for health workers
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ARMMAN partnered with Google Research and IIT Madras to design AI-based predictive modeling technology that provided an indication of women who were at risk of dropping out from our flagship programme, mMitra.
mMitra is ARMMAN’s free mobile-based voice call service that sends timed and targeted critical preventive care information directly to the phones of the enrolled women through pregnancy and infancy.
The prediction model is built on analysis of anonymised call records of women registered in mMitra.
call records
in mMitra
With support from Google Research and leveraging past call history data from mMitra, learning based models (Convolutional Neural Disengagement Predictor and Recurrent Neural Disengagement Predictor) were built to predict and identify participants most at risk of disengaging and dropping out from the mMitra programme.
These predictions helped us identify women requiring more involvement and interventions from health workers and counsellors. An easy-to-use dashboard for health workers was also developed to visualise predicted risk of drop-off.
A pilot was conducted in Ganjam and Sundargarh districts of Odisha for pregnant women who are Kilkari subscribers suffering from diabetes, hypertension and severe anemia. As part of the pilot, recorded voice calls with targeted content on these conditions were sent directly to the mobile phones of the women for 10 weeks.
The calls addressed topics such as :
- symptoms of the high risk conditions,
- danger signs,
- need for regular check-ups,
- nutrition related information,
- care during delivery,
- postpartum care, and
- adoption of healthier practices for long-term outcomes.
women have
completed the pilot.
A telephonic survey conducted with the first group of women who received the calls, informed that
health practices to manage their
high risk condition
to consuming more
nutritious meals
On completion of the pilot, a large scale research evaluation will be conducted and then we aim to integrate
the content into Kilkari 2.0.
The upcoming Kilkari 2.0 will leverage multiple delivery channels, including voice calls, WhatsApp, YouTube, to deliver targeted messages with richer multimedia content.
Women and children at low risk will receive nuanced voice calls and multimedia content, along with two-way communication through WhatsApp, depending on their access to smartphones.
Those with high-risk conditions and/or facing equity related challenges will receive targeted content,
two-way communication, and long-term support via call centre and WhatsApp.
The pilot to test the feasibility and effectiveness of WhatsApp as a delivery channel for Kilkari was conducted from May to October 2023 among 480 Women in Jharkhand and Haryana.
These insights are helping shape the development of Kilkari 2.0. The innovation will be scaled up to 2-3 states in 2024-25 and pan India from 2025-26.
The results from the pilot emphasise the effectiveness of videos, the reinforcing role of posters, and the importance of incorporating state-specific dialects in content.
Recently we won the Grand Challenges Grant for Catalysing Equitable Artificial Intelligence (AI), an initiative aimed at catalysing innovation to address urgent global health and development issues, funded by the Bill & Melinda Gates Foundation (BMGF).
This grant is helping us to integrate Large Language Models (LLM) – powered co-pilot into the existing learning and support application to improve the training of Auxiliary Nurses and Midwives (ANMs) so they can better manage high-risk pregnancies.
We have developed a multilingual and multimodal chatbot that responds to ANMs’ text and voice queries on high-risk pregnancies and antenatal care in English and Telugu.
Earlier, a Medical Officer would answer the doubts and queries of the ANMs through the learning and support application. The model has demonstrated an accuracy of over
93%
across all modules,
showcasing its precision in
addressing ANMs’ queries
70%
overall acceptance rate has been
shown for the support tool
with user testing with ANMs
A pilot was conducted in Ganjam and Sundargarh districts of Odisha for pregnant women who are Kilkari
subscribers suffering from diabetes, hypertension and severe anemia. As part of the pilot, recorded voice calls
with targeted content on these conditions were sent directly to the mobile phones of the women for 10 weeks.
The calls addressed topics such as :
- symptoms of the high risk
- conditions,
- danger signs,
- need for regular check-ups,
- nutrition related information,
- care during delivery,
- postpartum care, and
- adoption of healthier practices for long-term outcomes.
women have
completed the pilot.
A telephonic survey conducted with the first group of women who received the calls, informed that
On completion of the pilot, a large scale research evaluation will be conducted and then we aim to integrate
the content into Kilkari 2.0.
The upcoming Kilkari 2.0 will leverage multiple delivery channels, including voice calls, WhatsApp, YouTube,
to deliver targeted messages with richer multimedia content.
Women and children at low risk will receive
nuanced voice calls and multimedia content, along
with two-way communication through WhatsApp,
depending on their access to smartphones.
Those with high-risk conditions andor facing equity
related challenges will receive targeted content,
two-way communication, and long-term support
via call centre and WhatsApp.
The pilot to test the feasibility and effectiveness of whatsApp as a delivery channel for Kilkari was conducted from May to October 2023 among
and Haryana.
These insights are helping shape the development
of Kilkari 2.0. The innovation will be scaled up to
The results from the pilot emphasise the effectiveness of videos, the reinforcing role of posters, and the importance of incorporating state-specific dialects in content.
Recently we won the Grand Challenges Grant for Catalysing Equitable Artificial Intelligence (AI), an initiative aimed at catalysing innovation to address urgent global health and development issues, funded by the Bill & Melinda Gates Foundation (BMGF).
This grant is helping us to integrate Large
Language Models (LLM) – powered co-pilot into
the existing learning and support application to
improve the training of Auxiliary Nurses and
Midwives (ANMs) so they can better manage
high-risk pregnancies.
We have developed a multilingual and
multimodal chatbot that responds to ANMs’ text
and voice queries on high-risk pregnancies and
antenatal care in English and Telugu.
Earlier, a Medical Officer would answer the doubts and queries of the ANMs through the learning and support application. The model has demonstrated an accuracy of over
on shown for the support tool
with user testing with ANMs
showcasing its precision in
across addressing ANMs’ queries.
Prediction model using
Artificial Intelligence
Artificial Intelligence
ARMMAN partnered with Google Research and IIT Madras to design AI-based predictive modeling technology that provided an indication of women who were at risk of dropping out from our flagship programme, mMitra.
mMitra is ARMMAN’s free mobile-based voice call service that sends timed and targeted critical preventive care information directly to the phones of the enrolled women through pregnancy and infancy.
The prediction model is built on analysis of anonymised call records of women registered in mMitra.
call records
in mMitra
With support from Google Research and leveraging past call history data from mMitra, learning based models (Convolutional Neural Disengagement Predictor and Recurrent Neural Disengagement Predictor) were built to predict and identify participants most at risk of disengaging and dropping out from the mMitra programme.
These predictions helped us identify women requiring more involvement and interventions from health workers and counsellors. An easy-to-use dashboard for health workers was also developed to visualise predicted risk of drop-off.
Using WhatsApp as a delivery
channel for Kilkari 2.0
channel for Kilkari 2.0
The upcoming Kilkari 2.0 will leverage multiple delivery channels, including voice calls, WhatsApp, YouTube, to deliver targeted messages with richer multimedia content.
Women and children at low risk will receive nuanced voice calls and multimedia content, along with two-way communication through WhatsApp, depending on their access to smartphones.
Those with high-risk conditions and/or facing equity related challenges will receive targeted content,
two-way communication, and long-term support via call centre and WhatsApp.
The pilot to test the feasibility and effectiveness of WhatsApp as a delivery channel for Kilkari was conducted from May to October 2023 among 480 Women in Jharkhand and Haryana.
These insights are helping shape the development of Kilkari 2.0. The innovation will be scaled up to 2-3 states in 2024-25 and pan India from 2025-26.
The results from the pilot emphasise the effectiveness of videos, the reinforcing role of posters, and the importance of incorporating state-specific dialects in content.
High Risk pregnancy pilot for Kilkari
A pilot was conducted in Ganjam and Sundargarh districts of Odisha for pregnant women who are Kilkari subscribers suffering from diabetes, hypertension and severe anemia. As part of the pilot, recorded voice calls with targeted content on these conditions were sent directly to the mobile phones of the women for 10 weeks.
The calls addressed topics such as :
- symptoms of the high risk conditions,
- danger signs,
- need for regular check-ups,
- nutrition related information,
- care during delivery,
- postpartum care, and
- adoption of healthier practices for long-term outcomes.
women have
completed the pilot.
A telephonic survey conducted with the first group of women who received the calls, informed that
health practices to manage their
high risk condition
to consuming more
nutritious meals
WhatsApp chatbot for health workers
Recently we won the Grand Challenges Grant for Catalysing Equitable Artificial Intelligence (AI), an initiative aimed at catalysing innovation to address urgent global health and development issues, funded by the Bill & Melinda Gates Foundation (BMGF).
This grant is helping us to integrate Large Language Models (LLM) – powered co-pilot into the existing learning and support application to improve the training of Auxiliary Nurses and Midwives (ANMs) so they can better manage high-risk pregnancies.
We have developed a multilingual and multimodal chatbot that responds to ANMs’ text and voice queries on high-risk pregnancies and antenatal care in English and Telugu.
Earlier, a Medical Officer would answer the doubts and queries of the ANMs through the learning and support application. The model has demonstrated an accuracy of over