The science of complexity and lays a conceptual foundation for understanding “complex adaptive systems”. What all complex adaptive systems have in common is that they are all bound by the same set of physical laws. Their “behavior” i.e. growth, maintenance, and death can all be described using the same set of mathematical relationships. These systems (animals, plants, ecosystems, and etc.) are the most productive and functionally effective systems known to man. Unfortunately, our healthcare system has not been bound by the same physical laws and mathematical relationships as other complex adaptive systems. Thus, it has not been able to implement the same mechanisms that our ecosystems and cells have in order to obtain optimization in their ability to perform a function. This is most blatantly highlighted by the fact that 100,000–400,000 (depending on the reference source) Americans die each year due to medical errors. The integration of the science of complexity into medicine, nursing, allied, and public health is one course of action that would eliminate many of the issues currently present in our healthcare system.
For a complete detailed explanation, I recommend taking a course on complexity, or reading Geoffrey West’s book: Scale. I will summarize to say that understanding complexity is not about understanding the individual parts of a system and their properties, but understanding the manner in which the individual parts are arranged. How they are interconnected, and how this leads to a systems macroscopic behavior.
One of the most powerful technical accomplishments of the last decade was the development of neural networks. Prior to their development, things like SIRI and ALEXA would not have been possible. Neural networks were developed from understanding the arrangement of neurons not from understanding their individual properties. In order to solve the problems associated with our healthcare system, we need to understand the connectivity of its component parts and how the nature of that connectivity leads to its observable macroscopic behavior (medical error).
Complex systems have emergent properties
Something is complex if it contains some type of emergent property (something that cannot be explained with raw reductionism). One example of an emergent property would be consciousness (we cannot explain thoughts and imagination just by analyzing synaptic events in neurons). A second example of an emergent property would be the phase states of water: liquid, gas, and solid. Each of these contain the same individual parts i.e. water molecules, but the associated arrangement is different, and this difference in arrangement accounts for the systems macroscopic behavior. Ice is cold and melts, steam is hot and flies away, and liquid water is wet.
The most complex systems are also adaptive systems.
A system should be considered adaptive, if it has a capacity to process and integrate information. Following on our previous example: the central nervous system meets this criteria in that it processes and integrates information all the time. Water, regardless of the phase state, has no capacity to either process or integrate information. This discrepancy is due to the relative degree of complexity between the two. We can model the behavior of liquids fairly well due to their low complexity. We cannot model the behavior of the central nervous system, which is exponentially more complex.
Prior to his passing, Stephen Hawking came out saying that: “The science of the next generation will be the science of complexity.” Which indicates how critical the science of complexity will be in our ability to improve medicine by inducing changes in healthcare administration, provider education, and diagnostic testing based on its principles.
All complex adaptive systems have some form of a feedback mechanism in place to ensure they can reach a level of optimization organically. Balancing out growth, repair, and performance in such a manner that allows the system to be maintained while performing its function. Feedback mechanisms are scale invariant and substrate independent, meaning that they are universal. From intracellular enzyme cascades, to hormonal axis’ within organisms, to economies, and etc. feedback is absolutely essential for any complex system to adapt, maintain itself, or grow.
When feedback is ignored, the system will quickly spiral out of control and collapse: A cancerous cell ignores the feedback mechanisms that normally would trigger apoptosis. Beta cells in a diabetic person’s pancreas ignores the elevated levels of blood glucose. A wall street executive ignores feedback intrinsic to market forces and continues their excessive risk taking. All of these events happen at massively different scales and take place in unmistakably different forms, but they all have the same trend: when feedback is ignored the system collapses. The cancer grows, the blood sugar rises, the patient dies and the stock market crashes. Healthcare systems have little to zero feedback mechanisms in place. When they are implemented there is an almost immediate improvement. Understanding complexity science will enable Healthcare Administrators to better understand how we can most effectively implement these all too essential feedback mechanisms into the healthcare system enabling the system to perform its function effectively without being compromised by the demands for safety protocols.
Division of labor within a healthcare system
One of the major questions that complexity science answers is why plants, fungi, and animals all have their respected number of different cell types. Why not more specific specialized cell types? Why not less? Why does it seem to vary with the organism? These questions can be answered in many different frameworks within natural selection; one of these answers is in metabolic efficiency and the associated signaling required to achieve it.
We can use biological systems as the basis for mathematically representing the optimum point at which a system can divide its labor and benefit, both in the efficacy of resource conservation (energy used) and in its ability to perform the necessary function. This optimum point is critical: If further division of labor is used, systems spend too much time performing a function (and by extension cannot respond quickly enough to maintain homeostasis). If less division of labor is used, energy is needlessly wasted. There are many examples of this in the context of biological systems: from metabolic reactions that produce ATP, to the sub-cellular compartments, to the various different cellular signaling cascades for specific tissue and organ types.
An everyday analogy that we are all familiar with would be the size and distance of stairs in a staircase. Stairs are designed optimally to enable people to reach a vertical displacement in the most efficient physical manner possible. Add more steps and you’ll find yourself pointlessly taking longer to reach your destination. Remove steps and you’ll find yourself getting an unintentional quad and hamstring workout prior to reaching your destination. Straining your locomotion system beyond its optimal functional range. Sometimes to impossible points if, you suffer from some form of arthritis.
Unfortunately, our healthcare system currently does not follow this principle. As a former ED technician I can list multitudes of anecdotes to back up the study that overworking healthcare providers leads to a 40% higher probability of patient harm via medical error.
Complexity science, and specifically systems biology, lays the foundation for the use of guiding principles that will allow us to modify our current healthcare system into the appropriately dispersed networks that optimally divide up labor.
I once had the opportunity to interact with a nurse as she performed an NG tube insertion. As she was setting things up for the procedure I noticed that she placed the tube bare on the table tray adjacent to the patients bed. I also noticed that she was not wearing gloves as she applied the lubricant, nor did she wash her hands prior to doing the procedure. When I asked her why she didn’t wash her hands or don gloves she answered me with a quick “because the stomach is not a sterile place, you don’t need to worry about being clean.” When I specifically brought up the possibility of clostridium difficile infection, as endospores can survive incredibly harsh conditions. She responded by saying “c diff is a normal part of everyone’s gut flora, it only makes you sick if you take antibiotics.” (just for reference, here is a link to the many different hypervirulent strains that have been identified)
This single anecdote highlights one of the major problems with our healthcare system. Providers more often than not think in terms of “all or nothing”.
Medicine and nursing are not science, they are based off of it. Unlike biology or physics, there is no central unified theory of medicine. One cannot forget details about pathology or pharmacology; and through their own problem solving derive answers or missing information like a physicist derives equations from Newtonian laws of motion.
This means that students of nursing or medicine do not have the same set of guiding principles that one has when studying mathematics or physics, and as such, must rely on memorization as the primary means to manage the overwhelmingly massive (and quite frankly unnecessary) amount of information that they are required to study prior to entering their professions.
The innate and all too human response to learn new information and understand new concepts quickly; with low quality and high quantity is to simplify them into an ‘all or nothing’ framework. As was illustrated by the responses that the nurse gave me during our discussion.
Understanding complexity can provide a solution to the problems caused by ‘All or Nothing ‘ frameworks.
Complexity is all about understanding how all of the parts are interconnected, not the individual parts of a system. This framework does two things in the context of provider education:
- It provides students a means of universally applicable concepts that enable them to digest and understand the information required for their profession. As previously illustrated: cancers, diabetes, and other various pathologies are different substrates of a more abstract concept of a system ignoring feedback.
- It forces one to see things in terms of their dynamics and integration, rather than as blind linear reactions. Since complexity relies on emergent properties, it cannot be simplified into an all or nothing framework. This enables one to understand the critical details of a system: such as the relationships between a patient, a drug, a microbe, and an NG tube. All without being burdened by the unnecessary and excessive ‘baggage’ of the underlying details.
Diagnostic testing: it is rarely (if ever) an ‘all or nothing’ phenomenon.
One area of our healthcare system that I would predict complexity science to play a major role in would be improving the implementation of diagnostic testing. One major contributing factor to our failing healthcare system is in the provider’s inability to understand and interpret diagnostic tests. To be clear I do not blame the providers for this. Over the past decade, diagnostic testing and imaging have become more and more complex. What possible better solution could there be, than to integrate complexity science into their education?
Doing so would better prepare future providers to understand and handle those four dreaded words that almost always warrant a call down to the lab: “clinical correlation is recommended”. Complexity science could also improve feedback forces that assess for verification and validation when it comes to diagnostics. Which would also lessen the burden of healthcare costs
A very special thanks goes out to Geoffrey West at the Santa Fe Institute and Max Tegmark at MIT as their writings on AI and Complexity Science are what paved the way for the material in this article.