askNivi
Applying behavioral science, design, and technology to address consumer-facing health needs in emerging markets
As low- and middle-income countries transition from paper to digital systems, family planning programs can benefit from unprecedented opportunities to improve services. Investments in digital health tools have expanded exponentially, but information on what works—and what does not— remains limited and scattered. As investments have increased, digital applications and data fragmentation have proliferated, but stakeholders are moving towards more coordinated efforts to scale digital health solutions, support countries’ digital health infrastructure, and share evidence-based learnings.
This Digital Health Compendium enables users to explore case studies across a range of digital health technologies used to enhance family planning programs mainly in sub-Saharan Africa, but also in other regions of the world. Digital health applications in family planning programs can be broadly classified as those affecting demand generation, service delivery, supply chain management, and the policy and enabling environment. In many low- and middle-income countries, digital health innovations were adopted earlier in other health sectors, including HIV/AIDS, maternal and child health, and noncommunicable disease prevention and response. As a result, much of the impact evidence is likewise restricted to those sectors. To advance greater adoption of digital technology in family planning programs, more data and information on the challenges, opportunities, scalability, and results are needed. This compendium aims to consolidate emerging information and data on applications of digital technology in family planning programs to inform adoption and scale-up of successful approaches.
All of the case studies were submitted by the implementing organizations and include a description of the digital health intervention, program context, and, if available, important findings and lessons learned through rigorous evaluations or program data. The compendium facilitates a quick search for case studies based on the target user for digital health intervention, building block for the digital health enabling environment, family planning program classification, and country location. The case studies give policy and program decisionmakers insights on real-world applications of digital health, promising practices, challenges, and other lessons that can be applied to current and future programs.
Applying behavioral science, design, and technology to address consumer-facing health needs in emerging markets
Ben Bellows
Chief Business Officer
Nivi, Inc.
Email
Avenir Health
Marie Stopes Kenya
Population Services Kenya
Well Told Story
Packard Foundation
Jhpiego
Maisha Meds
Pathfinder International
Population Services International
MTV Staying Alive Foundation
Zana Africa
Merck for Mothers
Grand Challenges Canada
Packard Foundation
Kenya (2017)
India (2019)
South Africa (2020)
Nigeria (late 2020)
Global (early 2021)
Kenya, India, South Africa, Nigeria
Client, Health Care Provider, Data Services Provider
Demand Generation
Nivi is a consumer-facing digital health company offering actionable information to individuals via popular messaging channels like WhatsApp and Facebook Messenger. It makes it simple for men, women—and youth—to explore reproductive and maternal health topics that improve general knowledge and inform specific actionable guidance.
Nivi is a marketplace solution providing critical health information where and when consumers need it and generating actionable insights for our subscription-based customers, including non-profit charities, for-profit firms, and public sector health agencies.
Drawing from user experience design principles, behavioral science, and technology, Nivi offers an end-to-end solution for consumer health engagement that takes individuals through the various stages of behavior change—from awareness to education to action. This is accomplished through askNivi’s Engage, Chat, Act framework.
The Engage, Chat, Act framework allows subscription customers to see all the journeys users take from first engagement to most recent action and understand the key factors that drive behavior change. Because all interactions with askNivi are digital in nature (including the user’s self-reported facility experience), data is collected in real time, allowing partners to be more responsive to the realities of a particular market and adjust their activities accordingly.
During Engage, Nivi uses unique online digital links and keywords embedded in offline media to measure the effectiveness of each onboarding channel, from community health worker referrals to radio programs to Facebook ads. Overlaying conversion rates with user demographics, location, and related data allows Nivi and its partners to segment the engagement market and gauge the performance of specific campaigns.
As they enter Chat, Nivi users are prompted to state why they engaged Nivi before being routed to an appropriate conversation. A conversation provides each Nivi user with information on a specific health topic—or health product—and helps the user understand why that health topic or product might be relevant to them. For example, users interested in family planning can have a conversation about each contraceptive method, including how it works, and its benefits and disadvantages. These conversations serve as a precursor to taking action, as our research has found that users who engage in one or more of these conversations are more likely to want a recommendation for a contraceptive method. In this phase of the journey, partners can understand which pathways are most effective at guiding users towards taking action, and in particular, engaging in a special type of conversation called a screening conversation.
The screening conversation leads a user into the Act phase of their journey. In this conversation, the user is asked a few key questions to generate a recommendation for a health service or product. Currently, Nivi offers a family planning screening (based on World Health Organization standards) that serves as a decision support tool to help a user become truly informed about their contraceptive options and empowered to seek services. If the user is in a geographic area where Nivi has mapped out service providers (in the public health system or in the private sector), then the recommendation will also generate a referral to specific health providers where the user can obtain their desired method of family planning. The Nivi platform has built-in prompts that check in with the user at predetermined times to ascertain whether the user visited a recommended service provider, and if so, whether they obtained a method of family planning—as well as what their experience was at the facility. At this stage of the journey, partners are able to understand the breakdown of method recommendations, recommended facilities, any potential barriers to taking up a method, and for those who took up a method, what method they took up, as well as their experience at the facility.
Nivi is designed to generate data-driven insights throughout each user’s health journey. In 2018, using an early version of Nivi—when the platform had fewer than 10,000 users—Dr. Eric Green, Nivi co-founder and scientific advisor, ran a pilot randomized encouragement model to demonstrate how one could measure and attribute the effect of Nivi on contraceptive uptake. Women with an unmet need for family planning were recruited, and if they consented to participate were randomly assigned to receive a recommendation to try Nivi or to simply seek family planning at a public facility. Among those in the treatment group who tried Nivi, local average treatment effect was an increase in the probability of contraceptive uptake of 41.0 percentage points, although statistically non-significant (95 percent CI -0.03–0.85). As of 2020, Nivi now has more than 320,000 users in Kenya and more than 1 million users in India. The maturity of the platform and the much larger user base warrant revisiting the randomized encouragement trial.
Attribution of impact can also be understood from user experience. In early 2019, the Nivi team conducted a text mining analysis of 179,609 inbound and outbound messages in Kenya to characterize the ways that Kenyan men and women communicate about their health inquiries. The results of the study would inform Nivi’s future content development, tailoring, and automation, including the creation of a knowledge framework to link user intents and conversation topics. Externally, this analysis made two contributions. First, the research methodology offered researchers and practitioners a reproducible example of how to apply text mining techniques to text message interventions to assess feasibility, acceptability, and efficacy. The analysis also added to our understanding of how Kenyan Nivi users, in particular adolescents and young adults, converse on private networks about sexual and reproductive health topics.
The AskNivi team is preparing to publish findings of an analysis that explores the construct of “readiness to seek care” and tests the associations among a user’s self-perceived “importance of preventing pregnancy,” their self-declared readiness to seek care, and the likelihood they would report visiting a facility within two weeks. The analysis draws from routinely collected, anonymized health data submitted by Nivi users in Kenya. Preliminary findings suggest that while self-perceived importance of preventing pregnancy is not associated with care-seeking, self-declared readiness is.
The analytic deep dives in analyses of Nivi, coupled with routine insights shared on Medium (https://medium.com/nivi-inc), provide a growing evidence base on the impact of Nivi.
The world is moving online with more than 4 billion people globally now having access to the internet. Digital health will increasingly play a pivotal role, especially in those places where the older analog health systems have not been able to deliver services effectively.
However, digital health cannot simply copy the old best practices to succeed. Nivi generates value and revenue from supplying insights into trends and patterns in the user health journey from initial marketing engagement (“Engage”) to conversations accessed (“Chat”), and finally, to actions taken by users seeking health services, purchasing health products, or achieving behavior change outcomes (“Act”). Holistic and cost-effective transparency along each individual’s health journey will help to achieve meaningful health impact. Viable business models are needed for a significant portion of digital health to drive forward integrated self-care within the larger health system, empower patient-centered care, and support individual wellness.