Rita is a women's health company working to improve the information, transparency, and discussion around fertility. As a woman who had my own struggles with fertility, having the chance to work with a company that is supporting other women in this area is really fulfilling.
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Beginning with their first clinical study, involving over 1000 women, we were able to begin the process of analyzing holistic factors to understand how they correlated with fertility - a process that will continue to evolve and improve as more data is collected. With an expert team of clinical specialists in the reproductive space, we were able to create a set of 102 factors, derived from this questionnaire and based on the latest scientific knowledge, and produce actionable next steps for women who are starting or struggling with their journey.

We were also able to identify multifactorial patterns that were associated with various levels of difficulty with fertility. This is the early stages of looking at female health through a broader lens, while also getting the benefit of AI to locate low level patterns. In conjunction with the medical expertise, this work has the potential to offer a lot of insights into a process that can feel unclear and confusing to those going through it.

The breadth of this data comes with its own set of challenges, especially when it comes to interpretability. This is something that will improve as more data is collected, and Rita's product MVP is set up to do exactly that. They have launched an application that gives women access to a personalized care plan using the 102 factors that they developed. Through this application, they will be able to continue to collect holistic data throughout the fertility journey and use this data to further improve the insights that they provide to their users.

I'm incredibly grateful to have been able to be a part of this product. I lead the original clinical study, analyzed and model the data directly, and created a backend system that supports the insights - the factor definitions produced, as well as the probabilistic model used to assess the likelihood of challenges. The nature of this product, in that the factor definitions and modeling approach is expected to quickly evolve through continued iterations, created a fun challenge in how the backend system needed to be designed. Extensible code and externalizing logical definitions as metadata will enable the company, and the interpretation of its customers data, to quickly adapt to new insights.

I'm excited for all of the ways that this company will improve women's health and empower those that are reproductively able with information!
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