How to write Treatment Algorithms

Want to write clear and clinically actionable treatment algorithms that support oncologists and trainees in making decisions at the point of care? Read on!

Core Principles

1. Be Specific

• Clearly define the topic, cancer type, subtype and/or stage in the title and description

  • Example: "Stage IV Non-Small Cell Lung Cancer (EGFR-mutant)"

• Avoid overly broad algorithms that dilute clinical utility

• Include key stratifiers (e.g., biomarkers, performance status) where appropriate

2. Optimize for Clinical Decision Making

• Structure algorithms to reflect real-world workflow or a detailed part of the workflow

  • Initial diagnosis → staging → biomarker testing → first-line → subsequent lines

  • PET-CT → TNM classification  → Mediastinoscopy / EBUS → ECOG  → Tumor board discussion (oncology, radiology, pathology, surgery)

• Prioritize the decisions and action steps

3. Think Like a Trainee

• Assume your reader is synthesizing large volumes of information quickly and may not have your depth of knowledge in a certain disease space

• Where relevant, please include:

  • Brief context for each decision point

  • Key rationale (1-2 lines max)

  • Common pitfalls or exceptions

4. Incorporate Key Clinical Pathways

Include relevant sub-pathways when they materially affect management:

• Pathology workflows (e.g., biopsy adequacy, molecular testing)

• Biomarker-driven treatment pathways

• Surgical vs. systemic vs. radiation decision points

• Supportive care considerations, when relevant

Content Best Practices

Titles and Structure

• Use specific, searchable titles (cancer + stage + key biomarker)

References (SEO + Credibility)

• Include up-to-date, high-quality references on specific nodes where relevant:

  • NCCN, ASCO or ESMO guidelines

  • Landmark trials on published on NEJM, Lancet or PubMed

• This improves discoverability in ChatGPT, Claude, Google and more

• No need to add references for every step; this is where your clinical judgment and experience is important

Visual Design

• Add images and diagrams for better engagement:

  • PFS curves from pivotal trials

  • Tables for regimen comparisons and risk assessments

  • Pathology or imaging examples where relevant

• Keep your visuals simple and clinically focused

Algorithm Construction

• Use clear, concise nodes. For example:

  • Node title: “If EGFR mutation” → connects to child node: "osimertinib"

  • Node title: "If no actionable biomarker and PD-L1 ≥50%" → connects to child Node: "pembrolizumab monotherapy"

• Avoid ambiguity; specify thresholds and criteria

• Limit your title to 180 characters max for better readability

Here's an example from Dr. Paolo Tarantino at Dana-Farber Cancer Institute:

Early HER2 positive Breast Cancer algorithm by Paolo Tarantino, MD

Want more algorithms? Check out Open Medicine. 🙂