How to write Living Algorithms that actually help clinicians
- Apr 11
- 3 min read
Updated: 3 days ago
Good treatment algorithms don't just summarize information. They guide decisions.
The best algorithms are specific, patient-centered and structured around real clinical workflows so clinicians and trainees can quickly understand what to do next at the point of care.
Why most algorithms fall short
Treatment algorithms are everywhere in oncology, but many are difficult to use in practice.
They are often:
Too broad
Too abstract
Not aligned with real clinical workflows
As a result, clinicians still need to interpret, adapt and fill in gaps before making a decision. This defeats the purpose.
What a great algorithm actually does
A strong algorithm answers a simple question: what should I do next for this patient?
In order to do that, it needs to:
Start with the patient
Follow a clear pathway
Highlight key decisions
Provide just enough context
Not more information. Better structure.
Core principles for writing better algorithms
1. Be specific
Specificity is what makes an algorithm usable. Instead of broad topics like breast cancer or lung cancer, define the exact clinical scenario:
Stage
Subtype
Biomarker
For example:
Stage IV NSCLC (EGFR-mutant)
Early-stage HER2+ breast cancer
This immediately tells the clinician: "I'm in the right place."
It also improves discoverability in search and LLMs.
2. Optimize for clinical decision making
Algorithms should follow how clinicians actually think, not how documents are structured.
A typical workflow might look like:
Diagnosis
Staging
Biomarker testing
First line treatment
Subsequent lines
Or more detailed:
Imaging → staging → tissue confirmation → ECOG → treatment selection
The key is to prioritize decisions and action steps, not background information.
3. Think like a trainee
Many users are fellows, residents or community oncologists covering multiple diseases. This means they're synthesizing large amounts of information quickly.
Your algorithm should help them:
Orient themselves
Understand the logic
Avoid common mistakes
Where helpful, be sure to include:
Brief context for decisions
Short rationale (1-2 lines)
Common pitfalls or exceptions
Be detailed, but concise.
4. Incorporate real clinical pathways
Clinical care is not linear, and good algorithms reflect that. Be sure to include sub-pathways when they matter:
Biomarker-driven decisions
Surgical vs systemic vs radiation approaches
Pathology workflows
Supportive care considerations
This is especially important in complex cancers where cross-disciplinary specialties are involved.
Best practices for writing content
Titles and structure
Your title is critical. It should be:
Specific
Searchable
Clinically meaningful
Be sure to include:
Cancer type
Stage
Key biomarker, when relevant
This improves usability, as well as discoverability in ChatGPT, Claude, Gemini and other tools.
References: quality over quantity
References build trust and improve discoverability. Focus on:
NCCN, ASCO, ESMO
Landmark trials (NEJM, Lancet)
Add references where they matter most:
Key decision points
Pivotal treatment choices
You don't need references for every node. Clinical judgment still plays a role.
Visual design matters
Remember that clinicians think visually. The right images and video can:
Improve understanding
Speed up decision making
Support patient discussions
Useful visuals can include:
Survival curves
Treatment comparison tables
Risk stratification summaries
Imaging or pathology examples
Be sure to keep your images and video simple and clinically relevant.
How to structure your Living Algorithm
In medicine, clarity is everything. Each node should be:
Concise
Actionable
Unambiguous
For example:
"If EGFR mutation" → osimertinib
"If no actionable biomarker and PD-L1 ≥50%" → pembrolizumab monotherapy
Avoid vague language and be sure to specify:
Thresholds
Criteria
Exact conditions
This is what separates a poorly-designed algorithm from a valuable decision tool.
What you should avoid
Common pitfalls include:
Overly broad scope
Too much text per node
Lack of clear decision points
Missing thresholds or criteria
Trying to include everything
Remember: an algorithm is not a textbook, it's a pathway.
From information to action
The goal is not to capture all knowledge, it's to:
Guide decisions
Reduce uncertainty
Save time
A well-designed algorithm should allow a clinician to:
Find their scenario quickly
Follow a clear path
Act with confidence
A real world example
Algorithms developed by leading clinicians demonstrate what's possible. For example, early-stage HER2+ breast cancer pathways clearly map:
Risk stratification
Treatment sequencing
Decision points
See this example from Dr. Ilana Schlam at Dana-Farber Cancer Institute in HER2+ Breast Cancer:
This is the level of clarity that makes algorithms extremely useful in practice.
Bottom line
Writing a good Living Algorithm is not about adding more detail, it's about making the decisions clear. Be sure to:
Be specific
Follow real workflows
Focus on action
Keep it concise
When done well, algorithms become more than references. They become tools that clinicians can rely on every day.
Want to explore more?
If you're interested in how treatment algorithms can help real-world decision making, explore Living Algorithms on Open Medicine.
You may find they change how quickly you move from uncertainty to clarity.