Forget Moonshots: Biomedicine Needs an Air Traffic Control System
Jeff Shrager, PhD, Director of Research, Cancer Commons; Adjunct Professor, Symbolic Systems Program, Stanford University
Q: There never seem to be enough patients matched to cancer clinical trials to quickly test new cancer treatments. Might there be a better way, using new communication technology?
A: Among the few things that everyone can agree upon, one is surely that biomedicine is not an efficient engineering system — that is, a system that efficiently (in time, cost, lives, or whatever else you’d like to measure) reaches its goals to alleviate suffering, etc. When we think of efficient engineering systems, companies such as Apple, Boeing, Google, Tesla, NASA, GE, and Toyota come to mind. Perhaps Big Pharma and parts of the VA and military are efficient engineering systems but surely biomedicine, as a whole, is not.
Granted, biomedicine faces the highly complex problem of matching patients to treatments while at the same time creating and ethically testing novel treatments with budgets that are under constant pressure. But this problem is faced by a huge number of extremely smart people. What is the difficulty? I contend that the difficulty is, in large part, lack of coordination.
When I think of highly efficient coordination, I think of the Air Traffic Control system. The notable pain of air travel notwithstanding, one has to admit that the flight coordination is nearly magical! You can fly from New York to London in 5hrs for $500 while strapped into a 20x200ft aluminum tube (full of explosive fuel) traveling at 500mph, 50,000ft high in an airspace shared with 100,000 other flights doing the same thing in every direction all over the world. And you never think about dying! Well, you may think about it, but realistically you shouldn’t because air travel is spectacularly safe. Instead, worry about escalators and bicycles!
I contend that we can learn from the spectacular success of Air Traffic Control in order to improve biomedicine’s engineering efficiency by improving its coordination. Furthermore, I contend that we can do this without compromising ethics, whilst making everyone involved rich and happy–or at least happy that they are not pissing away money and lives.
Essentially, we have proposed to build a Biomedical Air Traffic Control System that will efficiently search the huge space of plausible treatment regimens, crossed with the relatively small number of available patients in ever diminishing cohorts. I call this approach Global Cumulative Treatment Analysis (GCTA), and we can build it on much more modern technology and with much better understanding of science and engineering than the aviation community had when they built theirs.
GCTA is described in the following video and in more detail in Theoretical Issues for Global Cumulative Treatment Analysis (GCTA).
Cancer Commons is putting together a series of virtual workshops to work out the practical details of GCTA. If you would like to be in the loop for these, please contact me at jshrager@stanford.edu.
Copyright: This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
A: Among the few things that everyone can agree upon, one is surely that biomedicine is not an efficient engineering system — that is, a system that efficiently (in time, cost, lives, or whatever else you’d like to measure) reaches its goals to alleviate suffering, etc. When we think of efficient engineering systems, companies such as Apple, Boeing, Google, Tesla, NASA, GE, and Toyota come to mind. Perhaps Big Pharma and parts of the VA and military are efficient engineering systems but surely biomedicine, as a whole, is not.
Granted, biomedicine faces the highly complex problem of matching patients to treatments while at the same time creating and ethically testing novel treatments with budgets that are under constant pressure. But this problem is faced by a huge number of extremely smart people. What is the difficulty? I contend that the difficulty is, in large part, lack of coordination.
When I think of highly efficient coordination, I think of the Air Traffic Control system. The notable pain of air travel notwithstanding, one has to admit that the flight coordination is nearly magical! You can fly from New York to London in 5hrs for $500 while strapped into a 20x200ft aluminum tube (full of explosive fuel) traveling at 500mph, 50,000ft high in an airspace shared with 100,000 other flights doing the same thing in every direction all over the world. And you never think about dying! Well, you may think about it, but realistically you shouldn’t because air travel is spectacularly safe. Instead, worry about escalators and bicycles!
I contend that we can learn from the spectacular success of Air Traffic Control in order to improve biomedicine’s engineering efficiency by improving its coordination. Furthermore, I contend that we can do this without compromising ethics, whilst making everyone involved rich and happy–or at least happy that they are not pissing away money and lives.
Essentially, we have proposed to build a Biomedical Air Traffic Control System that will efficiently search the huge space of plausible treatment regimens, crossed with the relatively small number of available patients in ever diminishing cohorts. I call this approach Global Cumulative Treatment Analysis (GCTA), and we can build it on much more modern technology and with much better understanding of science and engineering than the aviation community had when they built theirs.
GCTA is described in the following video and in more detail in Theoretical Issues for Global Cumulative Treatment Analysis (GCTA).
Cancer Commons is putting together a series of virtual workshops to work out the practical details of GCTA. If you would like to be in the loop for these, please contact me at jshrager@stanford.edu.
Copyright: This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
