In a 2021 study of Indian maternal mortality trends, the country’s maternal mortality rate is 99/1,00,000 live births – an objective outlined in the 2015 millennium development goal (1). Further, an estimated 1.3 million women have died from maternal causes in the last twenty years, mostly in rural areas (1). While healthcare innovation and reach of technology have reduced maternal deaths, many women cannot access existing healthcare facilities or face a dearth of resources.
In an attempt to capture India’s socio-geographical diversity, we conducted participatory research with women (aged 20-55 years) and their partners from at least 8 states, hailing from rural and peri-urban settings and belonging to the low-income category. Additionally, we spoke to medical and healthcare professionals to understand and map the existing ecosystems of care around a pregnant woman. A critical activity we conducted at this point was unpacking mental models, which unearthed similar inconsistencies in adhering to the doctor’s nutrition and lifestyle recommendations, lack of awareness of pregnancy warning signs, delay in decision-making and help-seeking. We mapped these behavioral patterns back to the socio-demographic culture and their positive/negative reinforcing feedback loops. We then did a causal layered analysis of social, medical, and cultural paradigms around pregnancy and women. These tools highlighted the system interconnectedness and helped us identify the main factors of adverse pregnancy outcomes.
We found a need for effective information dissemination and healthcare tracking methods that fit seamlessly into the lives of pregnant women from the rural poor, and develops their participatory interest in the pregnancy. The three-delay model that identifies the fatal gaps in utilizing healthcare services points to the delay in deciding to seek care (delay 1) as the largest contributor to adverse outcomes. This delay can be traced to a lack of information of pregnancy complications and risk factors, poverty and lack of education (2), thus supporting our conclusion.
We co-created a service with medical personnel that is embedded into existing traditions, with multiple organic touch-points and which leverages mobile penetration in rural areas. Pregnant women receive a sticker sheet at their doctor’s appointment that has stickers representing supplements, warning signs corresponding to their trimester, antenatal care appointments etc.
The women stick them to their calendars for the days that they take their supplements or experience those symptoms. At their next appointment, the doctors scan these stickers to gain insights into the patient’s pregnancy pains and medical adherence, leveraging Optical Character Recognition and machine learning. There are also two scannable stickers: one denote the next appointment and opens to a video on what the patient can expect at their next appointment, and the second connects to a playlist of concise and accurate informational animated videos on pregnancy best practices. Pregnant women and their partners can use it to empower themselves with accessible relevant information and become more involved. In the design and development of our intervention, we strived to be inclusive, conscious in our use of technology, and keep stakeholders at the centre of our design.