Kate Krontiris

Testing Assumptions About Women and Technology

Editor’s Note: Intel Corporation and Ashoka Changemakers are managing the online competition, She Will Innovate, to find the world’s most innovative solutions that equip girls and women with new digital technologies. This article originally appeared on the Ashoka Changemakers’ blog and is part of NextBillion’s Big Idea focus on mobile technology.

A reminder from the field: Always test assumptions.

Earlier this year, alongside a colleague from the Global Health Project at the MIT Sloan School of Management, I worked to determine how LifeSpring, a maternal health hospital in Hyderabad, India, might utilize mobile technology to gather data about its outreach efforts.

In the past few years, many mobile-based solutions have been developed to support health workers in low-resource contexts. (Some are better than others).

Many at LifeSpring, including outreach workers themselves, agree that the hospital could benefit from digitally collecting information about its clients. These records could help assess the effectiveness of outreach efforts by capturing indicators, such as the number of women who visit for prenatal care—a key goal of communications campaigns.

It might have been a good idea, but nobody was very excited about trying it. The reason: Most existing mobile health (mhealth) solutions are built on SMS (short message services, or simply text message) platforms, and LifeSpring was not confident about its outreach workers’ ability to use text messaging – or even about their ability to navigate and comprehend a text-based data collection system.

Even with the possibility of visual and/or audio prompts within a Hindi- or Telugu-language platform, the hospital was skeptical.

But technological capacity and textual literacy are not new concerns in mhealth, and LifeSpring nevertheless wanted us to explore the feasibility of a mobile solution to support its outreach efforts. To do so, we needed to better understand the intended users of a mobile data collection program.

Outreach workers are mostly middle-aged women who visit low-income neighborhoods to share information about LifeSpring Hospital with potential customers and check in on pregnant women that are due to deliver soon. We needed better intelligence about these women’s needs, behaviors, and capacities related to their responsibilities and to their use of technology.

We needed to better understand the skills and activities required of them in their roles as outreach workers, the data they currently collect, and their opinions about how technology may improve or hinder their work. We thus shadowed the outreach workers and conducted in-depth interviews with them.

Synthesis of these interviews in aggregate, as well as interviews with organizational stakeholders, was also done to help us understand Lifespring more broadly, including its capacity and will to implement a mobile solution.

One user we spoke to was a woman we will call Pooja. Having been with LifeSpring since its founding some seven years ago, she rotates among 25 different colonies (neighborhoods) associated with five different branches of the hospital, spending most of her time out of the hospital, interacting with new and returning customers. She returns daily to the hospital to share information with nurses and assist with customers in labor.

Pooja, like other outreach workers, has not received formal training for her position; rather, she has learned on the job, from other workers. Pooja is fluent in spoken Telugu, Hindi, Urdu, and reads and writes in English.

She is not unique: many workers we spoke with could also converse in three or four languages, and almost all of them write in English. In spite of this, Pooja and her fellow outreach workers were described by their Lifespring colleagues as low-literacy.

Pooja owns a basic Nokia mobile phone, which she uses mostly for personal communications, but she also uses it to contact her customers. She only uses the phone’s calling function and does not text message, although she says she would be willing to learn.

Based on what Pooja and some fellow workers said about their existing mobile habits, and what her hospital colleagues assumed about outreach workers like her, it seemed that LifeSpring’s outreach program was ill-suited for a mhealth solution. The major barriers, according to both groups, would be outreach workers’ low-literacy and lack of use of phone features beyond voice calls.

I am convinced that the most useful thing we did in this project was to test these assumptions.

We got in touch with Dimagi, a firm that develops user-friendly mobile data collection tools. With their help, we loaded a demo application onto basic mobile phones.

The SMS-based application had a series of Hindi-language forms with visual prompts designed for low-literacy users, and we proceeded to test it with outreach workers to understand their capacity and desire to use such a tool.

With a bit of basic explanation about navigating the application, most outreach workers we tested with – even though they only used their mobiles for voice calls at present – were able to read the prompts (“Full name,” “Husband’s name,” “Age,” “Date of last menstrual period,” etc.) and type in their responses. Simply through prototype testing, we were able to identify ready champions among the outreach workers; imagine what a full communications and training campaign could do.

The quick comprehension and ensuing confidence of the outreach workers reaffirmed the importance of testing assumptions. Though a full and tailored solution still needs to be developed for LifeSpring, we were happy to celebrate a small success at this point in the process.

We revealed new opportunities for LifeSpring by overcoming initial assumptions and biases about populations viewed as low-literacy, and recognizing that they – like the rest of us – are eager to embrace new tools.

Education, Technology
skill development