Data Asset Diligence: A Guide for Investors in Women’s Health Startups
The emergence of the femtech sector
In this era of free-flowing venture capital, innovations in the women’s health space are on the rise. In 2019, there were 20+ deals in the femtech space and over $800M in funding raised.[1] The global femtech market is set to reach a whopping $50B+ in 2025.[2] Investments in consumer healthcare, by way of physical clinics, have occurred in parallel to the growth of the femtech market, and they often intersect. Reinventing the female patient experience has expanded digitally as companies with and without physical space have identified the need to provide 24/7 accessibility in addition to scalability. Kindbody, for example, is a fusion of femtech and consumer health with its focus on reimagining the fertility clinic and providing digital tools to connect patients to their providers beyond the confines of the treatment room.
Data as a differentiator for healthcare startups
In a highly saturated and fragmented market, data can be a differentiator that may provide increased likelihood of the following:
· Exit opportunities
· Higher valuations
· Ecosystem of partners that would find value in the data produced
· Competitive advantage equating to power over buyers in a crowded market
· Unique contributions to research
The Data Value Framework
To evaluate a startup’s data assets, consider the following aspects of the data it collects:
· Use case: identify purpose for data collection or generation
· Data asset: clarify what makes this data different or better than existing sources. Imagine how this data is formatted within a spreadsheet by envisioning the rows, columns and cells. Differentiated data should include new variables (columns), enhanced descriptions of existing variables (cells), and/or a higher quantity or better variety of observations (rows.) If you lack subject matter expertise on the particular type of data, it is recommended that you liaise with an expert to confirm whether the company you are evaluating may produce a data set that is better than existing alternatives. Three types of data asset differentiation include:
o Extract, transform, load (ETL): the format of data that is blended from multiple sources and how it can be integrated
o Column differentiation: new variables are included in a data set that enrich its understanding and application
o Row differentiation: unprecedented volume and diversity of observations
· Commercialization: Commercialization refers to how the data can be shared or used with stakeholders such as; pharmaceutical companies, researchers, providers, payers and tech firms. There are lofty goals for big data in healthcare, including: diagnostics, preventative medicine, precision medicine, medical research, reduction of adverse medication events, cost reduction, and population health.[3]
Why is data relevant at a femtech startup?
Major technology players like Apple have picked up on consumers’ interest in “knowing thyself” and maintaining personal health records on their phone. Apple announced a long-term study with NIH and Harvard in September 2019 that will leverage Apple Watch data to monitor and evaluate critical women’s health concerns, such as Polycystic Ovary Syndrome (PCOS), infertility, and osteoporosis.[4] There’s even a straightforward onramp for consumer enrollment by downloading the Apple Research app on your phone and registering for the study.
Apple’s involvement in warehousing and analyzing this data is meaningful as historically, women’s health research has received little funding and women have been understudied in clinical trial settings.[5] As a result, conditions like PCOS and endometriosis that have a high prevalence are still not strongly understood and can often take a long time to diagnose.[6] While the collection of health data should be shared across sectors, companies do have an opportunity to fill institutional voids in clinical and pharmaceutical research.
Healthcare companies can use data to fill those gaps through the following innovation mechanisms:
1. Data “tidying”
2. Merging with other data systems
3. Gathering novel data
When evaluating women’s health startups, be on the lookout for companies that collect women’s health data in the following categories:
· Novel data or data that has never been collected across a diverse and statistically significant population — ex. menstrual cycle data collected by the thousands of period and ovulation tracking apps on the market currently
· Outcomes data that would provide insight to payers to improve reimbursement rates — ex. how earlier diagnosis on conditions that impact fertility like PCOS may lower long-run healthcare costs on fertility interventions like IVF
· Data from underrepresented demographics that addresses an inequity in the healthcare system — ex. qualitative data on hospital care providers and their treatment of black, transgender, Hispanic and Native American women during pregnancy and childbirth
· Data that is aggregated and “tidied” in a format that surpasses existing versions — ex. EveryMother collects data on diastasis recti and recovery from diastasis recti in a format that is likely easier to analyze than existing clinical data sources
Asking the right diligence questions on data assets
Data Innovation
1. Why were these data generated? Why are these data being collected?
2. What are the data type? Who entered the data?[7]
3. What is the time frame of collection?[8]
4. How exclusive or scarce is this data? How many sources is it collected from?[9]
5. Who are the customers for these data sets?[10]
6. What are the short term, medium term and long term outcomes and impacts of these data?[11]
7. Does this data offer new insight or understanding on a topic that is currently understudied?
8. What new variables are included in these data?
9. Are the volume of observations novel in some way?
10. Are these data better or more complete than existing alternatives?
11. What are the limitations of the data? How they will impact a value proposition to research or future customers of the data?
Data Privacy
12. Manage the downside risk of collecting data: privacy. How is this data being protected and what individuals within the firm has to the data?
Case Study: Maternal Mortality Information Collection and Sharing
There are many underlying issues in the healthcare system and U.S. society that enable prejudice to flourish in the way patients receive care. An important space for exploration may be the tracking of maternal mortality and information sharing around hospital treatment. Irth — a mobile application that “recognizes that implicit bias is a significant barrier to fair treatment for all” provides some of this data in anecdotal format.[12] Irth operates similar to yelp and enables users to provide reviews on how they were treated at hospitals. Sharing these experiences is an important step in eliminating the bias in care that has been directly linked to the high black maternal mortality and black infant mortality rates.[13] According to Irth’s founder, she believes that the “app’s back end data repository can be used to develop hospital ranking products, databases and research on C-section rates, breastfeeding rates and other measures of medical care across intersectional lines.”[14]
Proposed Diligence Questions to Ask Irth’s Founding Team
1. Data Innovation | Use Case and Differentiation: Your use case of enabling women to find quality care that protects their health at hospitals is a noble goal. I assume the app relies on solely patient-entered data. Does anyone peer review the entered data and factcheck or escalate concerns to providers?
2. Data Innovation | Use Case and Differentiation: How many hospitals do you collect data on? How many patient reviews do you have? Are patients incentivized in any way?
3. Data Innovation | Use Case, Differentiation and Commercialization: Thanks for sharing your commentary on the app’s back end data repository and your thoughts on how it might be used. Providers and health systems would certainly benefit from qualitative data on measures of medical care across intersectional lines. Do you have any studies planned that research the outcomes of women that selected their providers after reviewing Irth vs. a control of group of similar women that did not use Irth?
4. Data Innovation | Use Case and Differentiation: What data points do you currently collect on the women that are writing the reviews? Are there additional data points you seek to collect over time?
5. Data Innovation | Use Case and Differentiation: Are these data better or more complete than existing alternatives? Who do you see as your competitors in producing these data or offering this service?
6. Data Innovation | Use Case, Differentiation and Commercialization What are the limitations of the data? How they will impact a value proposition to research or future customers of the data?
Author note: These frameworks were compiled by the brilliant researcher, thought leader and professor, Lindsey Leininger as part of The Tuck School of Business at Dartmouth’s Healthcare Analytics and Society course.
[1] https://www.forbes.com/sites/estrellajaramillo/2020/01/08/femtech-2020-investors-trends-and-opportunities-in-womens-health-technology/#136548bb7d54
[2]https://www.forbes.com/sites/estrellajaramillo/2020/01/08/femtech-2020-investors-trends-and-opportunities-in-womens-health-technology/#136548bb7d54
[3] https://catalyst.nejm.org/doi/full/10.1056/CAT.18.0290
[4] https://www.apple.com/newsroom/2019/09/apple-announces-three-groundbreaking-health-studies/
[5] https://rtslabs.com/how-a-new-technology-industry-and-its-data-are-improving-womens-health/
[6] https://www.health.com/condition/pcos/pcos-diagnosis-delay
[7] file:///C:/Users/madel/Downloads/ey-value-of-health-care-data-v20-final.pdf
[8] file:///C:/Users/madel/Downloads/ey-value-of-health-care-data-v20-final.pdf
[9] file:///C:/Users/madel/Downloads/ey-value-of-health-care-data-v20-final.pdf
[10] file:///C:/Users/madel/Downloads/ey-value-of-health-care-data-v20-final.pdf
[11] file:///C:/Users/madel/Downloads/ey-value-of-health-care-data-v20-final.pdf
[12] https://birthwithoutbias.com/
[13] https://www.nationalpartnership.org/our-work/health/maternity/
