In one medical decision making paradigm, the clinical problem can be approached as a tree or an algorithm. Here, an algorithm does not refer to a "machine learning algorithm" in the computer science sense; it can be thought of as a structured, ordered set of rules to reach a decision. In this type of reasoning, the root of the tree represents the initiation of the patient encounter. As the physician learns more information while asking questions, they come to various branch or decision points where the physician can proceed in more than one route. These routes represent different clinical tests or alternate lines of questioning. The physician will repeatedly make decisions and pick the next branch, reaching a terminal node at which there are no more branches. The terminal node represents a definitive diagnosis or a treatment plan.

Here we have an example of a clinical management algorithm for weight and obesity management (National Heart, Lung, and Blood Institute, 2010). Each decision point (most of which are binary) is a diamond, while management plans are rectangles.

For example, suppose we have a female patient with several clinical variables that are measured: BMI = 27, waist circumference = 90 cm, and the number of cardiac risk factors = 3. Starting at node #1, we skip from Node #2 directly to Node #4, since the BMI > 25. At Node #5, again the answer is "Yes." At Node #7, again the answer is "Yes," taking us to the management plan outlined in Node #8:

A second example of an algorithm that combines both diagnosis and treatment is shown as follows (Haggstrom, 2014; Kirk et al., 2014). In this algorithm for the diagnosis/treatment of pregnancy of an unknown location, a hemodynamically stable patient with no pain (a patient with stable heart and blood vessel function) is routed to have serum hCG drawn at 0 and 48 hours after presenting to the physician. Depending on the results, several possible diagnoses are given, along with corresponding management plans.

Note that in the clinical world, it is perfectly possible for these trees to be wrong; those cases are referred to as predictive errors. The goal in constructing any tree is to choose the best variables/cutpoints that minimize the error:

Algorithms have a number of advantages. For one, they model human diagnostic reasoning as sequences of hierarchical decisions or determinations. Also, their goal is to eliminate uncertainty by forcing the caretaker to provide a binary answer at each decision point. Algorithms have been shown to improve standardization of care in medical practice and are in widespread use for many medical conditions today not only in outpatient/inpatient practice but also prior to hospital arrival by emergency medical technicians (EMTs).

However, algorithms are often overly simplistic and don't consider the fact that medical symptoms, findings, or test results may not indicate 100% certainty. They are insufficient when multiple pieces of evidence must be weighed for arriving at a decision.