Mining the insights of humanity.
From birth, parents raise us in different ways, teachers
teach us in different styles, and doctors treat us with different medicines and
give us different advice. These
experiences set us upon our paths in life, sculpting how we learn and how we stay
healthy. We can think of each
interaction between a teacher and a student, or between a doctor and a patient, as a little
miniexperiment, with an outcome that can be evaluated: Did the student learn and become able to use
the information to do creative and useful things throughout life? Did the patient improve in health and
develop proactive health-related behaviors?
With almost one million physicians, and about four million educators of
children, in the U.S. alone, we are as a society conducting millions of
perturbations of behavior every day.
However, we do not take advantage of the enormous amount of empirical
data that, in principle, could be collected and analyzed in the process. A tool for generating and mining such a
data set could not only reveal general empirical facts and principles about how
best to teach, or to prevent and treat disease, but also allow individuals to monitor their
own personal parameters that govern how they best operate, empowering them to
better themselves.
Consider the idea of an ongoing clinical
trial. Currently a clinical trial for a
drug involves, typically, a blinded test of a treatment versus a control, which lasts
a certain amount of time, and progresses in multiple stages, increasing the
number of people each time, and looking for certain outcomes. Then, if the trial ends successfully, the drug
can be sold. However, it's been observed
that a great many drugs likely work for only a fraction of the patients who
receive them. Indeed, drugs that may be bad if prescribed indiscriminately are sometimes useful for specific subpopulations (e.g., consider the story of thalidomide). Furthermore, after a drug is out in the world, it can be used
off-label by doctors. If side effects appear in a subpopulation of
patients, there isn't a forum to interactively analyze the properties of that
subpopulation in a rapid way. Clinicians can publish the results of such
observations in journals, but such observations often stand alone.
A complementary approach might be to continually
accumulate data about a drug as it is used to treat different diseases, in
different populations, over time. Each
individual patient would be permanently associated with a data point, so that
follow-up and further examination would become possible. As genomic information, brain imaging, and
other information-dense measures become increasingly cheap to acquire, tracking
multiple variables within a patient over long periods of time will become more
and more valuable, allowing one to find better predictors of future outcomes in response to a specific treatment,
and to derive conclusions that would be impossible from a limited snapshot of a
person's life. This could speed up the
process of testing out technologies, allowing evidence to be accumulated and
analyzed in a distributed and open fashion, and enabling cures to be developed and tested
faster. It could also simplify prospective studies, in
which patients are tracked before and after disease onset, say for conditions
such as autism or schizophrenia; right now it is very hard to do this because
detailed studies of people before a disease occurs are difficult for all but
the most common diseases. With
integrated database design and accessibility, it would become possible to
perform this analysis. Such a system
would also need to have instantaneous peer review that would occur in a
rewardable way; the system must track real identities and real reputations of people who comment on or synthesize insights from the database, to synthesize accountability, reputation,
and trust, and to separate the experts from the nonexperts. Perhaps free access to the
database's wealth of data would motivate people to contribute; people who contribute less, or who contribute lower-quality judgment, might instead pay to access it.
It's possible that this methodology could apply to other
domains of life, exploring how to assist people to become better--for
example, consider how to evaluate trajectories for the approximately two million inmates in U.S. prisons. Or consider mental health, in which many styles of
therapy are continually being explored by a diverse set of psychologists, therapists,
and psychiatrists. Or the economy: perhaps a way to help economies self-regulate is to build in self-analysis at
every step of the way, continuously generating models and testing theories to catch disasters before they happen.
How many approaches
to life ever get validated? When does a
strategy or method need to be personalized to an individual, and when is an
insight a general piece of wisdom? Systems that enable
these questions to be answered by providing a continuously updated snapshot of
the best practices of the world may change the way we live, and enable a new age
of rational decision making. "Those who
can't remember the past are doomed to repeat it." Well, currently that's just about all of us.
Cite as: Boyden, E. S. "Civilization as Experiment" Ed Boyden's Blog. Technology Review. 1/18/09. (http://www.technologyreview.com/blog/boyden/22512/).
Massachusetts Institute of Technology
Comments
do you think, for example, that the sellers of products like pharma drugs ACTUALLY want it to be revealed that they only work for some people, some of the time?
do you think the goal of producers is to make sure that their products meet real needs? or do you think that the producers of products, instead spend lots of money creating needs and then more money convincing consumers that their products are the best to meet these "manufactured" needs?
daaberg@yaho...
01/21/2009
Posts:1
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“Dr. Lisa V. Rubinstein, president of the Society of General Internal Medicine, said that sharing in decision-making “will help raise the quality of care given by any clinician, because it will sharpen the focus on the key decision points and help the clinician put a plan in place that the patient understands and agrees with.””
I couldn’t agree more. The issue, as I’ve said before, is not a doctor’s skill in deploying a set of prepackaged protocols to achieve a standardized outcome. It’s this exclusive focus on the proximal outcome that’s the problem – a problem made worse by evidence-based medicine. As a recent commentary in JAMA argues(1), through an emphasis on evidence-based guidelines, “perverse incentives may be introduced for clinicians to advocate treatments that are counter to what patients want and value.”
Patients don’t choose between discrete eventual states of a proximal outcome. They choose between different life-configurations. In other words, the goal needs to be to find a good engineering solution to kinks in a dynamic complex system, comprised both of medical and non-medical (life-trajectory, trade-offs) processes. I.e., to either raise the evolved state of that overall system to a higher equilibrium, or to keep it from sinking to a lower one (i.e., doing no harm). No disrespect to the medical profession, but physicians seem to have a “trained blindness” toward the non-medical elements, which by default seem to get conceptualized as subjective preferences, as opposed to objective case-specific facts. The medical focus remains on the proximal outcome, perhaps at the expense of the total system (at which point, harm is done). In contrast, a patient’s choices are usually aimed at optimizing that overall configuration - perhaps even if the proximal outcome is negatively affected (a.k.a. non-compliance). The danger there, of course, is incorrect estimation of eventual state due to a lack of medical training and information.
Now, non-medical life factors are just as knowable as biological facts, given that a patient can (pretty reliably) report on the former. But a doctor would probably suffer cognitive overload if he/she had to process that full complexity for each patient. So I suspect the friction will continue until the advent of decision-support algorithms tailorable to individual life-patterns. Perhaps also, life-configurations themselves are socially patterned rather than fully idiosyncratic, such that those patterns could be inputted into standard operating procedures.
On a tangent – this emphasis on top-down protocols is an enduring social pattern, common to most guild-based or collectivist systems, contemporary or historical. In contrast, in a true market-based system with competition between different professions, entrepreneurial solutions tend to be produced, to map onto the niches generated by the bottom-up distribution of empirical needs and preferences. In a guild-based system, practitioners tend to try and “moralize” those empirical needs into conformity with top-down protocols – as in, “This is what the patient should do/want.” Now, in my opinion, that’s misinformed. A doctor taking that approach has minimal case-specific data—on the intricate evolved system of trade-offs defining a life configuration—to work from. In addition, to my knowledge, medical training (as opposed to, say, behavioral economics) does not involve decision-analysis. Ergo, these judgments are really armchair speculation by untrained individuals. As such, they’re irresponsible. The only way to approximate knowledge of what a patient should do – what strategy would really make his/her life better – would be to run simulations and project eventual states, on both medical and non-medical dimensions.
bdas
01/22/2009
Posts:1