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The Pros and Cons of Algorithms in Emergency Care

One out of every two participants in ACLS courses surveyed stated that "memorizing the algorithms" was the single most important part of ACLS. Some find these statements chilling, and others agree.

But what exactly is an "algorithm"? If you consult a dictionary, it's likely to define an algorithm as "A process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer", or something along those lines.

Considering participants in ACLS courses are seldom computers, how applicable is this concept to the provision of emergency health care? The answer to this question may be seen in random explanations provided by ACLS students:


The concept of algorithms appears to have begun with computers. Unlike humans, a computer has fixed information pre-programmed into it. Hopefully, this information is adequate to address the variables it may encounter. For this reason, most algorithms are designed to only solve a certain type of problem, or accomplish a narrow set of tasks.

The Problem with Algorithms

Humans, on the other hand, may have very loose programming in the form of formal education, ranging from doctorate-level education with years of postgraduate clinical training to high school education and classes at the local fire station. Even if the curriculum is documented, what the student actually understands is generally not definable.

Because of these disparities, different providers can have vastly different levels of knowledge. Some understand the reasons behind each of the steps in the algorithm, and others simply follow them, unquestioning, even when that course of action is not best for the patient.

The concept that failure to deliver medical care by executing an algorithm created and published by volunteer organizations (some of which have questionable relationships with the pharmaceutical industry) constitutes poor treatment is simply untrue. Any active practitioner will eventually run into a situation where it would be appropriate to deviate from the algorithm.

Moving Beyond Algorithms

Determining proper medical care should not be based on recommendation "algorithms" whose use may be fraught with misunderstanding and misapplication. Establishing what is proper should be far more complex than a list of proposed treatments that may or may not be appropriate.

For many years, treatment recommendations appear to have more connection with convention than they do science or common sense. Competent practitioners must have a firm understanding of underlying anatomy, physiology, and pathophysiology.

Many ACLS participants fail to have adequate basic and clinical education to meet this first requirement. Now add pharmacology, clinical research, and a rather vast array of alternative interventions, and one might conclude that memorizing algorithms could never adequately prepare all practitioners. The well-educated clinician is capable of viewing a group of recommendations and, based on education, knowledge, and skill, find the best approach to specific clinical problems.

Weighing the Benefits & Dangers of Algorithmic Treatment

For practitioners whose education is limited, it is argued that the use of "algorithmic" treatment allows for rapid responses where higher levels of care do not immediately exist. Does this concept outweigh the misuse of misapplied lists? As is often the case, the typical response might be "needs to be studied further." Common sense might suggest another conclusion. 

Students of any discipline serve themselves better by seeking to understand material from the foundation on up. Starting with memorization of someone else's lists creates the possibility of action without knowledge. Obviously, a faulty and dangerous approach when applied to emergency medical care. 

Algorithms might be better used as "learning aids" rather than treatment protocols. There is no substitute for understanding through education. Treating a patient is complex, and should not be reduced to the use of prepackaged instructions.

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