Why oncology decision tools need to be specific (and less fluffy)
- Apr 23
- 3 min read
In oncology, general summaries are not enough. Clinicians need precise, actionable information to make decisions. Living Algorithms focus on specificity, turning evidence into clear guidance that can be used immediately at the point of care.
The problem with "accurate but vague"
Many clinical tools provide summaries that are technically correct. They might say:
A treatment is effective
Toxicities are manageable
Data is still maturing
All of this can be true, but in practice it's not enough.
When you're preparing for a patient, vague statements don’t help you decide what to do.
What clinicians really need
At the point of care, the questions are specific:
What are the key toxicities?
How common are they?
When should I adjust treatment?
What should I tell the patient?
General descriptions don’t answer these questions. Instead, clinicians need precision.
Why specificity matters
Small details often drive decisions. For example, "toxicity exists" vs. "25% of patients experienced grade 3 toxicity."
These are very different signals.
Similarly, "data is evolving" vs. "overall survival data is immature, but PFS benefit is significant."
Specificity provides clarity.
As Dr. H. Jack West said on X:
If you have deeper knowledge, you know that guidelines are a witch's brew of strong evidence, weak data and faith-based bias. Ideally, you won't follow them slavishly and can deviate based on your good judgment and insight.
The risk of fluffy content
When content is too general, clinicians may:
Ignore it
Look elsewhere for detail
Miss important nuances
Even if the information is accurate, it doesn't change behavior. If it doesn’t change decisions, it has limited value.
Where this shows up most
Treatment rationale
High-level summaries often lack actionable insight. Clinicians want to know:
Why this treatment?
In which patients?
Compared to what alternatives?
Toxicity and side effects
Saying a drug has side effects is not helpful. Clinicians need:
Which toxicities matter most?
How severe are they?
How often do they occur?
Clinical caveats
General caution statements don’t guide decisions. Specific caveats do:
Which patients are at risk
What thresholds matter
What to monitor closely
How Living Algorithms provide specificity
Living Algorithms are built around actionable detail.
Precise data where it matters
Instead of general summaries, you see:
Hazard ratios
PFS and OS
Relevant percentages for adverse events
Clear, concise rationale
Rationale is focused on:
Why this treatment is recommended
What the key drivers are
What limitations exist
No unnecessary filler.
Actionable toxicity information
Side effects are presented in a way that supports decisions:
Common vs. severe side effects
Frequency of adverse events
Clinical relevance
Structured decision points
Each step is defined by:
Clear criteria
Specific thresholds
Unambiguous actions
Less text, more signal
The goal is not to remove information, it's to increase signal.
Less filler
More relevance
Faster understanding
This is especially important in time-constrained settings.
Designed for real clinical use
When you open a tool before seeing a patient, you don't want a summary. You want:
The key facts
The important risks
The next step
Living Algorithms provide this specificity.
From description to decision
There's a difference between describing a treatment and enabling a decision. Description informs you, but specificity guides you.
Living Algorithms are built to guide.
Bottom line
In oncology, precision matters. General statements don’t support real decisions.
Clinicians need clear, specific, actionable information they can trust and use immediately. Living Algorithms deliver this level of detail, helping you move from understanding to action.
Try it with your next case
The next time you review a treatment summary, ask yourself: "does this tell me exactly what I need to do?"
If not, it's missing the level of detail that clinical care requires.
That's where specificity makes the difference.