Data-Driven Medicine Will Help People — But Can It Do So Equally?

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Technological advances threaten to make a crushingly unequal system even more so.

The promise of data-driven medicine is clear. Using the latest analytical techniques can lead to better health outcomes and — over time as data technology inevitably becomes cheaper and more widely available — help many more people. But as medicine moves from the kind of clinical practice that has informed centuries of treatment to the data-driven practices that have already transformed commerce, finance and the media, it will also find itself facing some of the same social challenges. In particular, big-data technology might seem like a social neutralizer or even a leveling force, but it can have a way of increasing divisions.

One hint at why this is comes from what communications theorists describe as a knowledge gap. Basically, people who already have better information are also better at getting more information, even if that information is in theory universal and available to all. We see this again and again in different fields. In my own research on schools and computers, for example, I often encounter students doing advanced and creative “technology” activities on the computers in well-off schools, and students doing rote learning and typing on the computers in poorer ones. That division means that later on, when the kids face a putatively even playing field, some will know better than others how to get ahead. Privileged kids get more resources not simply because they (or the schools) can afford to pay for them but because their parents are better equipped to advocate for their acceptance into gifted and talented programs, or to academically support them better through tutoring, attention and encouragement — harder tasks for a poor or single parent. There is also the effect of expectations and a lifetime of socialization: If you experience life as unfair, you are probably less likely to demand better when you encounter more injustice.

There is a great lesson here as we anticipate the rise of data-driven diagnostic and intervention techniques in health care. It’s not that new methods won’t help people; it’s that they will increase health inequality — not just among those who can afford it and those who cannot, but among those who can undertake the research and take advantage of the new techniques and those who cannot.

Further, these new data-driven medical techniques could lead to more discrimination. If there are no legal restrictions, for example, what’s to stop companies from trying to hire people who have fine-tuned their sleep patterns with biofeedback, who have better exercise outcomes thanks to genomic analyses or are less likely to develop cancer in the long run?

Legislators are not unaware of these problems. In 2008, the United States passed a landmark law called the Genetic Information Nondiscrimination Act, which bars companies from hiring, firing or promoting workers based on genetic-test results — or requiring such tests — and insurance companies from requiring or using such tests to decide coverage. But legislative protections are easily reversible. In fact, last year Republican lawmakers introduced a bill that would carve out several significant exceptions to the law.

It may seem perverse to worry about inequality when we are talking about something that can improve so many lives, but a society isn’t held together by making wonderful things available to just a few rich people. This isn’t an argument for holding back improvements in health care. It is an argument instead that we must focus on equitable outcomes for all of us: how to make sure that access to new forms of health care is fair and evenly distributed; how to make sure that we guard against new forms of discrimination that can emerge from all this data; and how we avoid a corporate-driven version of these shifts, in which health outcomes are mobilized not for our happiness and our well-being but to squeeze another hour or two from us at work.

Considering that the United States is alone among developed nations in its refusal to recognize health care as a fundamental right, it’s even more crucial to recognize that inequality risks poisoning the fruits of American medical ingenuity. The right measure for successful health care isn’t about the maximum possible for a few but the average for everyone, the median for a society and the minimum opportunities available to even those with the fewest resources and privileges. That’s not just fairness. That’s what a healthy society looks like.

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