In the Nicomachean Ethics, Aristotle writes, “for what the doctor appears to consider is not even health, but human health, and presumably the health of this human being even more, since he treats one particular patient at a time” (I.6). If Aristotle is right, the aim of medical praxis is ultimately not that of the human, but of singular humans. Despite research in narrative medicine and other forms of patient-centered care confirming the wisdom of Aristotle’s claim, we are today surrounded by arguments and assumptions that the best way to serve individuals is by leveraging knowledge of groups. That is to say, it is in an unparalleled breadth and depth of knowledge about population-level phenomena that Big Data solutions, whether in the service of building biobanks for All of Us^™ or leveraging digital health humanities for grant funding streams, ground their promises.

This is in part due to the fact that medicine, once an art, is today in many respects and in many contexts more akin to a science. There is a constant temptation, more powerful in some domains than others, to envisage the study and application of medicine as best suited to the methodologies and epistemologies of the natural sciences or quantitative social sciences. As with particle physics, would not medical efforts be improved by discovering a grand unified theory—in this case a Grand Unified Theory of Human Health paired with an equally Grand Unified Big Database? Never mind the rabblerousing data on placebo and nocebo effects. Or on health outcomes pertaining to the irremediably qualitative and contextualized issue of patient-provider communication. Or the ever-present concerns about industry influence on data-gathering and peer-reviewed results. If we get all the data we need and tweak our methodologies just right, we will find the True Path to health. But whose health, exactly, will that be? Which particular, singular humans will be better healed, cured, or cared for by following such a path?

We live in a world where politicians reliably garner votes by ignoring the poor, professing to support the middle class, and ramrodding barefaced legislation to further enrich the 1% amidst one of the largest wealth gaps in the USA’s history. We also live in a world where modern medicine is taken as an achievement that proves humankind’s progress and even enlightenment, whilst nearly half a million people from across the globe died from malaria in 2016. 40 million Americans live in poverty (12% of its population) and 42 individuals hold same wealth as the 3.7 billion poorest in the world. These facts are not unconnected. The NIH’s budget in 2018 is slated to be $36.1 billion. How much scholarly ink will be spilt and public ire raised over which people are served by these funds? And, far more importantly, will this turn to outrage and political action when compared to the over $800 billion dollars that may end up allocated to the department of defense this year alone?

The intersection of big data and human health will continue to raise hard and complex problems. There is one problem, however, that has and will never be easily solved: how to best judge its unflagging promises. History teaches time and time again that new technologies always appear with new promises to fix old problems or thwart impending ones. As Olivia Banner astutely notes, the type of promises Big Data and its offshoots make are as old (if not older) than the internet itself. We would be wise to heed lessons learned just decades ago.

One might at this point worry that I have painted too negative of a picture. In a time of gargantuan global inequality, sprawling food deserts, pharmaceutical-company and legislatively-induced epidemics, and other forms of systemic classism, racism, and economic vulturing, there is unquestionably a need for us to better understand population-level phenomenon and to gather more and better data. The digital humanities indeed harbor unique potentials to enrich the ways we practice medicine and study health, especially with respect to the study of its history. As contributors to this question have pointed out, it already has in multiple ways. But let’s not kid ourselves: if Childress and Beauchamp’s principles are still held to be at the core of biomedical ethics, justice is losing the battle in the principled struggle for health for all. No amount of data will decide this ethics fight because principles, values, and their bearers are neither composed of, nor easily persuaded by 0s and 1s.

I am thus sympathetic with Travis Chi Wing Lau’s warning “to remain critical of the progress narratives attached to ‘Big Data’” and with Jarah Moesch’s worry that the extent to which Big Data solutions focus on altering individual behavior, the moreso such solutions miss the forest for the trees and are liable to reinforce extant stigmas and oppressions. My concern is that digital, technological interventions into what is ultimately an analog, human practice cannot promise to bring about substantive change for those whose lives are least valued and most at risk today. That promise is a promise of justice and of changes to our values, not just our inputs, outputs, or bandwidths. It is with that promise in mind that I hope the humanistic aspect of digital humanities leads the way, for both the USA and the world are in dire need of a more humane sense of health.