For all the ways that technology has visibly transformed our lives as consumers over the last decade, it has seemed like just a matter of time before the excitement of big data, social, local, mobile, process automation, artificial intelligence, and blockchain (nb. use of buzzwords intentional) will make their way into helping us meet the aims of precision medicine and population health. Though I am quite convinced that health care as an industry can be one of the most rapidly changing, I think it is fair to say that the health care consumer (ie patient) experience has remained fundamentally unchanged during this period. It feels, if anything, that the gap is only getting wider. What’s taking so long?
Reflecting on this question has been a pastime for me<span> for many years. I’ve since become convinced that a few things are not responsible for the delay. Given the steadily increasing investment activity in this space, it is no longer easy to make a case that this is due to lack of interest. Each year brings greater levels of investment and greater maturity to existing and emerging players. H/t to Rock Health for continuing to beat this drum in a nice graphic.
There are many ways one can attempt to blame regulation as a contributor to industry complexity and unpredictable consequences of smart innovation. This may be a part of the picture, but these are rules of our own creation and they can be learned. I’ll intend to reflect more on this another time.
Instead, for today I would like to propose that much of the delay in tangible improvements to patient experience with digital health reduce to biological complexity. To get there I want to start obliquely with what has become a recent fascination for me: the development of standards. Consider for a moment how much of your life depends on standard forms or knowledge: USB, credit card size, metric system (or not), traffic signage, electrical outlets, basically everything about your Internet web browser experience, zippers! Health care is full of guidelines, but really lacks robust standards.
When systems are of our own design, the standards process is basically a political one: arriving at consensus. Off-standard systems can be redesigned to accommodate consensus at some finite cost. For complex systems not of our own design, reengineering is not really an option. Developing the “check engine light” for our bodies or Human API are useful analogies, but are challenged by a biological system that repeatedly resists our “improvements” and defies our attempts to standardize.
Every person is unique. This is obvious from our daily interactions with people, but it bears mentioning because it runs counter to medicine’s biomedical model (BMM). The BMM suggests that there is enough we have in common as humans that generalizes across variations in outward appearance, personality, behaviors, genetics, and so on. Much of the meaningful variation is invisible, but enters into view when you know where to look. I’m mostly referring to genetics, but a fun example of this is the palmaris longus muscle. To illustrate, hold your hands out in front of you with palms facing up. Touch the pads of your thumb and pinky finger together. Do you see a line pop up at the center of your wrist? Some people have this muscle and some people don’t. Or you’re like me and have it on one wrist only (source: Wikipedia).
Given just how much polymorphism exists across these dimensions, when you stop to think about it, it is remarkable that the BMM fits at all! How strange is it that the truly unique genetics/epigenetics of one individual can give rise to a cancer arising from a single unique cell of that unique individual and then stepping into the next room have another unique individual with a different clonally-proliferating unique cell population that just so happens to respond to a drug that targets a common cell-signaling pathway. The paradox of this precision medicine is that it is valuable because it is imprecise. It is thankfully more than happy accident that when we deteriorate, we do so similarly. Yet, for as much as we know about disease, it is clear that there are many orders of magnitude more that we don’t know. In tech, we often move these known unknowns into the known knowns through standardization. The process is complicated, but tractable. Human biology, on the other hand, is not a willing partner in the attempt to reach a standard. It is beyond complicated—it is complex.
Amidst this complexity, interventions lead to unpredictable results—frequent lessons in humility when grappling with medical knowledge. This turns out to be as true for interventions made out of bits (ie software) as it is for interventions made out of atoms (ie drugs). The initial salvo of digital health tools (including my own), spurred on by the pace of tech change in other industries, attempted to streamline or even circumvent the discovery process. Many to their own detriment.
Fatalistic though this may seem, I am optimistic. The health care industry has the most important assets at its disposal: an infusion of fresh ideas, people willing to take a long view through lifelong commitment, and a mission that is nothing short of the reduction of human suffering.