During the 10 years we have worked together, John and I have written often about the power of words like transformation, optimism, cynicism, and misdiagnosis. Another word that needs more attention is “interdisciplinary.” It’s been uttered so many times in science, medicine, and technology that it’s lost much of its impact. We all give lip service to the idea, but many aren’t willing or able to do the hard work required to make it a reality, and one that fosters innovation and better patient care.
Examples of the dangers of focusing too narrowly on one discipline are all around us. The disconnect between technology and medicine becomes obvious when you take a closer look at the invention of blue light emitting diodes (LEDs), for instance, for which Isamu Aksaki, Hiroshi Amano, and Shuti Nakamura won the Nobel Prize in Physics in 2014. While this technology reinvented the way we light own homes, providing a practical source of bright, energy-saving light, the researchers failed to take into account the health effects of their invention. Had they been encouraged to embrace an interdisciplinary mindset, they might have considered the neurological consequences of being exposed to too much blue light. Certain photoreceptive retinal cells detect blue light, which is plentiful in sunlight. As it turns out, the brain interprets LEDs much like it interprets sunlight; in effect, telling us it’s time to wake up, making it difficult to get to sleep.
Problems like this only serve to emphasize what materials scientist Ainissa Ramirez, PhD discusses in a recent essay: “The culture of research … does not incentivize looking beyond one’s own discipline … Academic silos barricade us from thinking broadly and holistically. In materials science, students are often taught that the key criteria for materials selection are limited to cost, availability, and the ease of manufacturing. The ethical dimension of a materials innovation is generally set aside as an elective class in accredited engineering schools. But thinking about the impacts of one’s work should be neither optional nor an afterthought.”
This is the same problem we face in digital health. Too many data scientists and venture capitalists have invested time and resources into developing impressive algorithms capable of screening for disease and improving its treatment. But some have failed to take a closer look at the data sets upon which these digital tools are built, data sets that misrepresent the populations they are trying to serve. The result has been an ethical dilemma that needs our immediate attention.
Consider the evidence: A large commercially available risk prediction data set used to guide healthcare decisions has been analyzed to find out how equitable it is. The data set was designed to determine which patients require more than the usual attention because of their complex needs. Zaid Obermeyer from the School of Public Health at the University of California, Berkley, and his colleagues looked at over 43,000 White and about 6,000 Black primary care patients in the data set and discovered that when Black patients were assigned to the same level of risk as White patients by the algorithm based on the data set, they were actually sicker than their White counterparts. How did this racial bias creep into the algorithm? Obermeyer et al explain: “Bias occurs because the algorithm uses health costs as a proxy for health needs. Less money is spent on Black patients who have the same level of need, and the algorithm thus falsely concludes that Black patients are healthier than equally sick White patients.”
Similarly, evidence from an Argentinian study that analyzed data from deep neural networks used on publicly available X-ray image datasets intended to help diagnose thoracic diseases revealed inequities. When the investigators compared gender-imbalanced datasets to those in which males and females were equally represented, they found that, “with a 25%/75% imbalance ratio, the average performance across all diseases in the minority class is significantly lower than a model trained with a perfectly balanced dataset.” Their analysis concluded that datasets that underrepresent one gender result in biased classifiers, which in turn may lead to misclassification of pathology in the minority group.
These disparities not only re-emphasize the need for technologists, clinicians, and ethicists to work together, they also beg the question: How can we fix the problem now? Working from the assumption that any problem this complex needs to be precisely measured before it can be rectified, Mayo Clinic, Duke School of Medicine, and Optum/Change Healthcare are currently analyzing a massive data set with more than 35 billion healthcare events and about 16 billion encounters that are linked to data sets that include social determinants of health. That will enable us to stratify the data by race/ethnicity, income, geolocation, education, and the like.
Creating a platform that systematically evaluates commercially available algorithms for fairness and accuracy is another tactic worth considering. Such a platform would create “food label” style data cards that include the essential features of each digital tool, including its input data sources and types, validation protocols, population composition, and performance metrics. There are also several analytical tools specifically designed to detect algorithmic bias, including Google’s TCAV, Audit-AI, and IBM’s AI- Fairness 360.
The fences that divide healthcare can be torn down. It just takes determination and enough craziness to believe it can be done — and lots of hard work.
This piece was written by Paul Cerrato, senior research analyst and communications specialist, and John Halamka, MD, president, of Mayo Clinic Platform.