{"id":27386,"date":"2018-11-08T19:24:25","date_gmt":"2018-11-09T00:24:25","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/lily-health-using-machine-learning-to-scale-access-to-reproductive-health-advice\/"},"modified":"2018-11-08T19:24:25","modified_gmt":"2018-11-09T00:24:25","slug":"lily-health-scaling-access-to-reproductive-health-advice-with-machine-learning","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/lily-health-scaling-access-to-reproductive-health-advice-with-machine-learning\/","title":{"rendered":"Lily Health \u2013 Scaling access to reproductive health advice with machine learning"},"content":{"rendered":"

Introduction to Lily Health<\/strong><\/p>\n

Lily Health is an early stage start-up in Nairobi, Kenya that provides women with sexual and reproductive health advice via mobile messaging. While there are a large number of companies that provide women\u2019s health information and period tracking through mobile applications, there was no equivalent solution for women in developing countries who don\u2019t have access to smartphones and instead rely on SMS messaging. Moreover, in many developing countries, women lack access to information about family planning and reproductive health due to stigma surrounding these topics and\/or access to the internet or healthcare facilities. Lily provides a subscription service that allows women to text questions to their friend \u201cLily\u201d and receive responses based on a database of information about reproductive health as well as personalized information about that woman\u2019s cycle. The company currently operates in Kenya but has plans to expand other markets, including India, Bangladesh, Brazil, and the Philippines [1].<\/p>\n

Lily\u2019s Machine Learning Strategy<\/strong><\/p>\n

When Lily initially launched, employees were manually providing responses to customers [2]. This manual process is not a scalable solution and would have prevented Lily Health from scaling and reaching the 10M+ young women they hope to eventually serve [3]. Machine learning will enable Lily to create an automated and scalable product that responds to customers using a database of customer details, reproductive health information, and a steadily improving algorithm that interprets customer questions in order to generate accurate responses \u2013 a \u201cchat bot\u201d solution.<\/p>\n

The management team was forced to slow growth in order to focus on the development of a more robust chat bot. Lily\u2019s customer base quickly grew to a size that exceeded its current capacity. The management team recognized the importance of building a robust technological solution and froze all new customer acquisition while focused on product development. In addition, the company developed a hybrid product \u2013 a combination of predictive database responses and human response curation \u2013 for use while it refines the product. Because the product has real impacts on women\u2019s health, it is important for Lily to be cautious about the quality and accuracy of its responses during early product testing. In order to support the product under development, the management team has also invested significant capital in upgrading its back-end technology platform. Storing and processing large volumes of data requires a much more professional tech stack than the team\u2019s original \u201cbootstrapped\u201d capabilities [2].<\/p>\n

In order to prepare for the medium term, the company has increased its fundraising and talent recruitment efforts. As this solution scales and moves to new markets, the amount of data Lily processes will increase significantly, requiring talent with significant experience managing complex machine learning algorithms. In addition, Lily will require substantial additional capital to expand its technology infrastructure. Lily is designed specifically for developing countries where technology infrastructure often presents additional challenges and higher costs than a similar product might experience in established markets.<\/p>\n

Recommendations<\/strong><\/p>\n

While machine learning automation will enable Lily to scale, it also presents a number of ethical and safety concerns as there is a risk of providing individuals with inaccurate information that could impact their reproductive decisions. In addition, there are cultural differences and societal norms that Lily may need to take into consideration as it enters new markets. There are a few actions Lily Health should take to help safeguard against this risk.<\/p>\n

First, Lily should increase its in-country partnerships with healthcare providers and organizations. Lily has established partnerships with a few doctors and organizations, but should continue to build out this network in order to build credibility and bring in supporters who can help improve the quality of information provided.<\/p>\n

Second, Lily should establish (if it hasn\u2019t already) a comprehensive process for testing and updating its technology. Regular testing should be established to ensure information accuracy and to continually make adjustments that could improve the overall speed and effectiveness of the algorithm.<\/p>\n

Open Questions<\/strong><\/p>\n

Technologies like Lily Health often provide \u201cleap frog\u201d solutions to populations that lack traditional access to certain information and services. What responsibility do companies like Lily have to address the underlying issues driving the need for their product (e.g. internet access, social stigma around reproductive health)?<\/p>\n

What additional steps, if any, can Lily Health take to make sure it is providing accurate and safe information to its customers?<\/p>\n

Word Count: 714<\/p>\n

 <\/p>\n

References:<\/p>\n

[1]Emil Sjoblom and Macgregor Lennarz. Lily Health Merck Pitch Presentation, May 2018.<\/p>\n

[2] Emil Sjoblom. Interview by Leah Coates, August 2018.<\/p>\n

[3] Lennarz, Macgregor. \u201cHow might we radically improve access to, and quality of, sexual and reproductive health education and services for young people?\u201d <\/em>OpenIDEO, December 2017. https:\/\/challenges.openideo.com\/challenge\/youth-srh\/ideas\/lily<\/a>, accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"

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