Clinical Algorithms and Practice Guidelines

Definition

Algorithms are branching-logic pathways that permit the application of carefully defined criteria to the task of identifying or classifying different types of the same entity. Clinical algorithms are often represented as schematic models or flow diagrams of the clinical decision pathway described in a guideline.

Clinical findings, diagnostic test characteristics, and treatment options are abbreviated into their basic components. Algorithmic flow diagrams are then constructed as branching logical pathways with decision points represented as yes/no nodes. Such a flowchart sequence is useful in identifying or classifying entities based on carefully devised criteria (Figure 1). Application of clinical algorithms is most defensible when the evidence supports choices in the decision tree. Although very useful for clinical decision making, algorithms cannot account for all patient-related variables. Therefore, algorithms are not intended as a substitute for the clinician's best judgment.

Proposed Standards

The Society for Medical Decision Making Committee on Standardization of Clinical Algorithms has proposed certain standards for construction of clinical algorithms. Their technical note has specific recommendations on the types and shapes of algorithm boxes (clinical state box— rounded rectangle; decision box—hexagon; action box—rectangle; and link box—small oval), titles, abbreviations, annotations and their format, and schemes for arrows, numbering, and paging.

Classification

Management Algorithms

Management algorithms encompass both diagnostic and treatment modalities. They employ decision-relevant yes/no question nodes. Each question node in turn leads to an instruction node, denoted by a single exit arrow. Instruction nodes advocate for specific interventions. Thus, patients get classified into distinct clinical subgroups that would benefit from specifically targeted management strategies.

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Figure 1 Schematic model of a management clinical algorithm

Note: Management algorithm for a patient with a hypothetical set of presenting symptoms/signs.

Outcome studies are essential to providing support for each management strategy.

Validity and Flexibility

It is often argued that algorithms are not always backed by empirical data, are infrequently linked to the literature, and are not adequately flexible when dealing with clinical uncertainties. To circumvent these inadequacies, two important modifications have evolved.

First, in an attempt to enhance the validity, annotated management algorithms have been devised. Here, each node concerned with specific findings, characteristics, or interventions is annotated with the intent of summarizing the guideline's detailed textual material. The textual material is in turn replete with citations. Thus, in this fact-based approach, the algorithm links the recommendations of the guideline to systematic literature reviews or, when appropriate, to expert consensus. The Agency for Health Care Policy and Research guideline development process exemplifies this approach.

Second, counseling and decision nodes are specifically implanted in the algorithm where therapeutic decisions are expected to be constrained due to a gap in current knowledge. This is particularly relevant when patient preferences vary with respect to two (or more) different therapeutic options (e.g., medical vs. surgical management). At each decision node, the expected outcome associated with each option is indicated to the extent possible. Thus, counseling and decision nodes facilitate therapeutic deliberations between physician(s) and patient.

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