Opening the Healthcare Sector’s Eyes to its Data Blind SpotBy Guillermo Pepe | Mon, 04/11/2022 - 13:00
Started in October 1948 in the city of Framingham, Massachusetts, the Framingham Heart Study is arguably one of the most influential medical studies in history. Before then, almost nothing was known about the epidemiology of cardiovascular disease. The study, which began with 5,209 adults and is now on its third generation of participants, has resulted in over 3,000 peer-reviewed scientific papers. What’s more, it’s still ongoing.
Outstanding in its scope and duration, it is thanks to this unprecedented data collection that we gained much of the now-common knowledge concerning risk factors for heart disease, such as smoking, obesity, sedentary lifestyle and poor diet. The Framingham Heart Study is even the source of the term “risk factor” — before it, doctors had little sense of prevention.
This is what science applied to medicine does best: conducting research that becomes data with the potential to profoundly impact our lives. Data’s power is huge and true in all levels of healthcare, from your own doctor’s advice to the development of drugs to the implementation of public policies. New technologies, such as AI and machine learning, have only increased this power. It is thanks to data that we have been able to prevent, diagnose and treat all kinds of diseases, from cancer to diabetes to Alzheimer’s and, for the past two years, the COVID-19 pandemic.
However, in over a decade of experience as an entrepreneur and tech innovator in the healthcare industry, I have come to the realization that these efforts to get more and better data have suffered, in most cases, from a severe blind spot.
It all comes down to one crucial factor: the complete, complex real world we live in is not included (not even regarded) in the data. Why? Because, over the last century, medical research and clinical trials have tried to reproduce the sterile, aseptic conditions of a science lab in an attempt to achieve “experimental control.” This implies, naturally, the need for modern, high-tech centers, which in most cases are located in first-world countries or urban and privileged areas in developing countries. Last but not at all least, the people who participate in these trials are chosen based on their biological and clinical conditions only, thus underestimating social, psychological, economical and environmental factors.
As an example, let me “double click” on just one possible factor and see what happens: the rural factor. With 45 percent of the world’s population living in rural areas, and 80 percent of the extreme poor and 75 percent of the moderate poor living in those very same rural areas, it is simply not realistic to talk about real-world data if almost half the planet is not included in the collection of this data.
I founded Mamotest in 2013 with the belief that underserved women in Latin America —
both rural and urban — had the right to access high-quality care. I took on one of the most painfully unresolved issues: every year, 685.000 women (70 percent of them from developing countries) die of breast cancer even though, if detected at an early stage, the disease has a 93 percent or higher survival rate. We created the first telemammography network for breast cancer diagnosis in the region, a successful company with social impact that has been recognized by the United Nations, Harvard University, and the Inter-American Development Bank, and recently became winner of the prestigious Zayed Sustainability Prize.
The greatest challenge so far hasn’t been installing mammography units in remote locations, such as the Gran Chaco area, South America’s second-biggest forest after the Amazonia (and called “The Impenetrable” by the locals for a good reason). Nor has it been hiring the best medical professionals — anywhere in the world they might be — to provide a telediagnosis in real time. Our actual ordeal has been getting these underserved women to actually show up at our centers.
Why? The reasons are multiple. For instance: fear of pain, fear of embarrassment, fear of testing positive. To be fair, these are motives that any upper-middle- or higher-class woman could claim, too. But, for low-income to medium-low-income women, there are many more. Just to name a few: the impossibility of leaving their children alone at home, the threat of losing their jobs (usually informal employment), and the belief that it would be an immoral thing to do, sometimes reinforced by their own husbands who reject the idea of having another man laying hands on his wife, no matter if he’s a medical professional.
To find solutions to these issues, we needed to be very creative. (Some years ago, we invited the Archbishop of the Catholic Church to one of Mamotest’s centers so he could bless a mammography unit. It worked: the next day, the demand for testing peaked like never before.) And there’s more: The same old barriers that these women face just to get tested — and some new ones, too, such as the bureaucracy of the healthcare system in Latin America — appear along the long, arduous journey of breast cancer treatment.
It is only by obtaining and analyzing real-world data that we can generate insights that make a difference. By understanding the real barriers these women face, for instance, we were able to promote a law in the northern provinces of Argentina and the Yucatan state in Mexico that allowed them to take a day off work to get tested.
And since we have access to these women like no other organization has had before, we are now implementing Bolder, our state-of-the-art AI-driven platform that will track the evolution of half a million breast cancer patients in Mexico and Argentina in 2022 at every stage of their journey. Our goal: to provide the first structured, real-world data set of breast cancer patients in Mexico and Argentina, and to replicate it later worldwide. Our conviction: It is only through a deep knowledge of this historical data blind spot, and the leverage that technology provides, that we will be able to reinvent healthcare for all.