What is a Medical Algorithm? Medical Algorithm Definition

  • This article explores the medical algorithm definition. What it means in healthcare, how it is used, and the various forms it may take on.

What is a Medical Algorithm? Medical Algorithm Definition


“Algorithm” is a term that has a long and rich history in mathematics. However, it is a term that may have different meanings for different audiences.

Medical Algorithm Definition

An algorithm is any method for solving a problem or achieving a specific goal. It has one or more finite steps, each of which may have one or more operations. Each operation is executable (manually by a user or by a computer). The algorithm requires any number of inputs (including none) and can have one or more output(s). For non-random operations, the same input should always give the same output. This feature means that an algorithm will be consistent, which is attractive for health care documentation. Our focus in this article is on medical algorithms. What forms of medical algorithms exist and how are they used in healthcare?

Algorithms can be classified based on
(1) type
(2) complexity
(3) purpose or function
(4) manner of execution

Types of Medical Algorithms

Equation or Formula:
One or more numerical values undergo mathematical operations to generate a final output or score.

Table Driven:
Selected variables are matched to values in a table to generate a specific output.

Encode or Decode:
Certain findings are translated into a code, or a code is deciphered back to its original meaning.

Nomogram or Other Analog Representation:
A tool that replaces a complex formula with a simple visual representation that can be solved by hand.

Criteria Matching:
Using certain key findings for classification.

A simple construct based on one or more if-then statements.

A series of tasks that must be completed or “checked off.”

A series of questions, often used for data collection.

Flow Diagram or Decision Tree:
From a starting point the user proceeds along a tree-like structure with data-driven branch points until a terminal node is reached.

Medical Algorithm Complexity and Their Uses

The complexity of a medical algorithm may range from trivial to highly complex. The more complex the algorithm the more difficult it is to maintain. One way to simplify maintenance of a complex algorithm is to divide it into subtasks, each using a less complex algorithm embedded under an overall control structure such as a flow diagram.

Functionally an algorithm can be used:
(1) to classify
(2) to diagnose
(3) to aid in decision-making
(4) to determine prognosis and risk
(5) to specify therapy
(6) to monitor for response to therapy
(7) to determine cost-effectiveness of a strategy
(8) to trend data over time
(9) to perform data conversion

An algorithm may also be characterized as being manual, semi-automated or fully automated. A manual algorithm is executed entirely by the user. For example, a nomogram requires the user to visually tally points for various factors then translate this score to a probability of an event. An automated algorithm operates without user intervention, often triggered by some change in the patient’s electronic health record (EHR). A semi-automated algorithm may require one or more direct user inputs which may not be available in the EHR or which may require a clinical assessment.


About the Author:

John Svirbely, MD is a founder and Chief Medical Officer of The Medical Algorithms Company and the primary author of its medical algorithms. John is a co-founder of the Medical Algorithms Project and has developed its medical content for nearly 20 years. He has a BA degree from the Johns Hopkins University and his MD from the University of Maryland. He is a board-certified pathologist with a fellowship in medical microbiology and biomedical computing at Ohio State University. Dr. Svirbely recently retired from private practice and resides near Austin, TX. He has authored multiple books and articles on medical algorithms & medical calculators.