Simplicity and Entropy in Emergency Medicine
- May 12th, 2015
- Daniel Cabrera
Author: Daniel Cabrera, MD (Assistant Professor of EM / Associate Program Director, Mayo Clinic) // Editor: Alex Koyfman, MD (@EMHighAK) & Justin Bright, MD (@JBright2021)
The job of an Emergency Physician (EP) is difficult. Every shift we balance the difficulties of making a decision with suboptimal information and resources amidst a sea of chaos. From a cognitive point of view, the main job of an EP is to organize the information in the Emergency Department (ED) to make safe and appropriate interventions and disposition decisions.
Emergency Departments are Complex Adaptive Systems (CAS), where all elements are linked to each other and behave in a unified and complex way to respond and adapt to internal and external stimuli. The CAS of the ED is based on the patients, their biological behavior, their discursive narrative about their illness, the data emanating from biochemical and imaging tests, the state of the healthcare staff as individuals and as a group, as well as the resources available in the ED or in the hospital as a whole, and finally the environmental inputs to the ED such as patient registrations, weather conditions or multiple casualty incidents.
As surprising as it may appear, from a decision making theory, the world of the CAS ED is not a rational one. In order for a decision to be completely rational, all possible data and scenarios need to be considered to achieve the optimal answer. That is not possible as we suffer the constraints of our brain processing capacity, availability of information, time limitations and investigative resources (test) scarcity; this is called bounded or limited rationality.
The main challenge of EPs is to make the best possible decisions in a medium that is chaotic by definition and constrained by time, resources, and our own ability.
A way to approach this dilemma is to abandon an optimization approach to decision making and to adopt a satisfying strategy, where the goal of the decision making is not to find the perfect answer to a problem but a reasonable next-best solution that allows us to make a safe, appropriate, and economical decision that will benefit our specific patient and the overall micro-universe of the ED.
We have to lower the entropy and complexity of the decision.
Information entropy was defined by Claude Shannon as the uncertainty contained in a given set of information and Andrey Kolmogorov defined complexity as how much a set of information can be compressed / simplified as a function of the information contained in the set. These concepts sound complicated, but the translation to clinical EM is that when dealing with information and searching for solutions, you need to try to find the essence / core of the question / problem and make every effort to minimize risk / uncertainty / entropy.
Reducing the complexity and minimizing the uncertainty of the problem, we can make safe and appropriate decisions in the best interest of our patients.
The single most important skill to solve a problem is to understand the problem. We commonly face clinical situations that we have faced before (thunderclap headache or respiratory failure), where as a function of familiarity we can address with reasonable solutions from a previously developed armamentarium of solutions.
Understanding a problem depends on identifying the key components of the problem:
- Preferences/values or goals
- Rules constraints
- Possible alternatives/actions
Significant time needs to be spent identifying these problem components, as they provide meaning and a context to the problem. When the problem has been framed correctly, the next step is to isolate the core problem and develop the solution.
Parsimony is a virtue in problem solving. In an environment constrained by time, resources, and brain power; satisfying strategies appear to be more efficient than optimization strategies. Carefully selecting the minimal meaningful goal (the correct test or the correct disposition) is a cornerstone of complexity reduction. Often it is efficient to deconstruct a large or complex problem into small meaningful problems and find appropriate solutions for them in a serial fashion.
When dealing with a problem, the key for efficient and efficacious decision making around the problem is to try to spend the time and resources trying to identify what makes the problem unique. Oftentimes we spend time trying to make the problem fit a determined mental model even if the problem actually does not fit in it (ask yourself what doesn’t fit / what could I be missing). Identifying and dealing with the unique characteristics of a decision will guarantee that the core issues have been addressed and the quality of the solution will be better.
Novice problem solvers believe that the more information available for the analysis, the better the solution for the problem will be. This concept has a lot of caveats; certainly we can’t deal with vast amounts of information in a limited time frame, as our brain and resource constraints will not avail for the computing necessary for the solution. Also, not all information is equal; large amounts of information often contains redundant and meaningless data that only clogs the pathways of decision making and requires work to separate the wheat from the chaff. More information is not better, more meaningful and discriminatory data is better. Time needs to be spent identifying and separating the meaningful information from the noise.
A fair amount of the essence of a problem lays in the context. It is important to remember that it is the context that gives meaning to the problem and not the other way around. We know that abdominal pain in an elderly gentleman is different from a teenager and also we know that frostbite is not expected to happen during summer in Florida. Understanding the interaction between the concept and the problem allows us to create a strategy appropriate for them as these reflect usually two constraints that we have to abide by in our practice: resources available and external rules.
When a problem appears to be insurmountable, remember to switch to a satisfying strategy and try to reduce the complexity to obtain the next best available solution. Clinical problems are more complicated than they look but simpler than you think.
Sources and further reading
- Mitchell M. Complexity: A Guided Tour [Internet]. Oxford University Press, USA; 2009.
- March JG. Chapter 1: Limited rationality. Primer on Decision Making: How Decisions Happen. Free Press; 2009. p. 1–56.
- Kolmogorov complexity [Internet]. Wikipedia, the free encyclopedia. 2015 [cited 2015 Mar 10]. Available from: http://en.wikipedia.org/w/index.php?title=Kolmogorov_complexity&oldid=650742041
- Entropy (information theory) [Internet]. Wikipedia, the free encyclopedia. 2015 [cited 2015 Mar 10]. Available from: http://en.wikipedia.org/w/index.php?title=Entropy_(information_theory)&oldid=650621149