The Financial Upside of Hard-Science Female Founded Companies

Written by Spring DigitalDx Venture Fellow Joanna Zhu

In 2020, women comprise 43% of the U.S. STEM workforce for scientists and engineers under 75 years old. For those under 29 years old, women comprise 56% of the science and engineering workforce.

Yet, only 8% of women are focused on pushing the frontier on the hard sciences. However, of these female led startups, they achieve 35% higher returns for investors than their male only counterparts.

The next Bill Gates, Steve Jobs or Elon Musk of our time will be a woman. She understands how to solve a specific problem facing this world by leveraging tech. She knows the diverse and inclusive team it will take to build the product and implement solutions. She’s an experienced entrepreneur who knows how to operate with the leanest resources because, let’s face it, she’s had no choice considering how VCs have invested 98% of their capital in startups led by men. And the women-led startups that did raise capital, on average, raised 36 times less money in 2017 than those founded by men. In this paper, we go in depth to understand why focusing your investments in diverse and female founders is the right move.

Thesis:

• Women are the primary caretakers within their household and are the main decision makers as it relates to family healthcare.
• Post-COVID: Female founders, who typically fundraise later in their company stage, can better meet investors’ altered focus and receive proportionally more funding than previously (generally companies are further along)
• Women investments generate higher returns for investors at 35% vs. male funds

Why Women

DigitalDx Ventures is one of the pioneer funds turning the tide by focusing on early-stage, majority women-owned startups leveraging AI and big data technologies to diagnose major global health issues. Make no mistake though, DigitalDx is not funding women-led startups as a charity. We invest in women founders because we understand the strong ROI of investing in diverse startups.

Private technology companies led by women are:

• More capital-efficient, achieving 35% higher ROI on aggregate in the US, while Women founded companies in First Round Capital’s portfolio outperformed companies founded by men by 63%.
• Achieve 12% higher revenue than startups run by men, according to the Kauffman Foundation.
• Despite the severe funding gap, startups founded by women actually performed better over time, generating 10% more in cumulative revenue over a five-year period, according to BCG.
• Generating a Return on Equity of 10.1% per year versus 7.4% for those without strong women leadership, according to MSCI ESG Research.

Investors set a higher bar for female-lead teams to get funded and the market doesn’t recognize that explicitly yet. Women are a net positive on a founding team from a diversity of thought and experience perspective. Now is the right time to invest in diverse teams.

Why Healthcare AI & Preventative Care:

The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. From a socioeconomic perspective, the failure of the system to provide basic, affordable health solutions continues to be a major driver to the growing gap between the highest and lowest echelons of our country. Diseases such as cardiovascular disease and mental illness continue to impact those in poverty at an increased rate, often due to lack of early detection.

As AI diagnosis becomes more prevalent, we must ensure that data can be leveraged not only for economic policy purposes, but also for health diagnosis and treatments as well, in order to maximize the efficacy of resources deployed from impact investors.

With healthcare costs representing roughly 18% of US GDP, it is not surprising that this market attracts considerable investor interest. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans

We believe widespread data digitization in healthcare provides a favorable backdrop for expanding implementations of AI & ML, which have the potential to transform many aspects of patient care:

Computer Vision to Index/Tag:

In healthcare, inconsistent data is a major issue — with computer vision, AI can instantly ingest product images to consolidate and combine inconsistent data, classify products using standardized taxonomy, generate contextual identifier and tags and represent visual information (organs, cells) in a way that is ingestible for search, analytics, and other technology and data solutions. In healthcare, many applications within diagnostics are based on radiological image analysis, though some involve other types of images such as retinal scanning or genomic-based precision medicine. Since these types of findings are based on statistically-based machine learning models, they are ushering in an era of evidence- and probability-based medicine.

Neural Networks and Deep Learning:

In healthcare, the most common application of traditional machine learning is precision medicine — predicting what treatment protocols are likely to succeed on a patient based on various patient attributes and the treatment context. The great majority of machine learning and precision medicine applications require a training dataset for which the outcome variable (eg onset of disease) is known; this is called supervised learning such as:

• Decoding functional gene networks for cancer therapeutics discovery
• Discovering rare antibodies previously ignored by humans
• Ability to quickly analyze the entire genome of cancer patients

AI Forecasting Simulation

The hardest part of healthcare diagnostics is the counterfactual of “what could have been”. With improvements in AI forecasting, doctors are provided the ability to construct a simulation of a potential surgery, medical treatment with optimized key parameters to play out different scenarios

Natural Language Processing/ Voice to Text Transcription:

In healthcare, the dominant applications of NLP involve the creation, understanding and classification of clinical documentation and published research. NLP systems can analyse unstructured clinical notes on patients, prepare reports (eg on radiology examinations), transcribe patient interactions and conduct conversational AI.

Why DigitalDx

DigitalDx Ventures is an early-stage, majority woman-owned impact fund comprised of a team of successful Silicon Valley digital health investors and medical professionals leveraging AI and big data technologies to diagnose major global health issues (such as breast and other cancers, cardiovascular and kidney health, Alzheimer’s, and mental health) with a particular focus on:

• Enabling early, more accurate detection
• Minimizing invasiveness
• Expanding access to treatment through lowered costs

Collectively, the DigitalDx Ventures team has invested (directly or through previously managed funds) in over a dozen companies, which today have a combined value of well over $3 billion. The fund partners have had six exits (at 3x to 14x multiples) and have close to 200 patents to their credit.

The fund partners have deep ties to Stanford, UCSF, major medical institutions for clinical testing, startup communities, and the women entrepreneurship community; and all partners participate and judge competitions for organizations such as SPADA, StartX, and UCSF Incubator.

Working with a world-renowned expert on Artificial and Decision Intelligence, the DigitalDx team has internally developed an investment decision-making model called the Investment Tool for Expert Diagnostics (“iTED”) to capture relevant factors in evaluating potential investments, encourage greater adherence to key principles of successful outlier companies, and create discipline to aid in the consistency and scalability of analysis.

The DigitalDx team is uniquely qualified to source, diligence, invest, scale, and exit cutting-edge digital health companies and have thus far invested in six game changing investments:

Company 1 images the retina to identify Alzheimer’s disease 8–10 years prior to brain damage or dementia by measuring the beta amyloid buildup in the brain. Only Alzheimer’s diagnostic to receive Breakthrough FDA Designation.

Company 2 analyzes urine to identify kidney transplant rejection and kidney health with a very high degree of accuracy, utilizing its patented urine extender

Company 3 improves diagnosis, treatment, and outcomes in patients with brain-based disorders, including Autism, ADHD, depression, anxiety, and addiction disorders.

Company 4 analyzes saliva to identify breast cancer, with saliva sampling and gene expression profiles utilized as a complement for callbacks.

Company 5 has developed a congestive heart failure solution that provides a non-invasive, direct, and absolute measurement of congestive heart failure exacerbation through radio waves.

Company 6 has developed a multi cancer screening blood test that identifies in which organ system cancer is located, including lung, liver, stomach/gastric, pancreas, colon, cervical/ ovarian, and prostate cancer.

Diversity is not a nice to have, it is a necessity:

Diagnostic errors affect approximately 12 million U.S. adult patients each year, according to a 2011 study published by the U.S. National Library of Medicine. Such errors could harm patients and also make physicians more vulnerable to medical malpractice claims. Various issues could lead to diagnostic errors, including misinterpretation of clinical studies, narrow diagnostic focus, inadequate or inappropriate testing, failure to adequately assess a patient’s condition, failure or delay in obtaining a referral or consult and overreliance on a previous diagnosis.

And in 2020, we see that by not considering the full scope of the user problems of a particular demographic, it can lead to adverse outcomes — such as skin cancer treatment recommendations not being optimized for different ethnicities of women make it clear that diversity is not always included in the ideation phase or clinical trials, let alone product design. For a great product to be created, it requires the founder to have real user empathy for the pain points of their constituents, and that is fundamentally why diversity will always be a winning bet.