What should I do next? How Living Algorithms answer oncology questions at the point of care
- Apr 10
- 4 min read
When you're seeing a patient, you don't need more data. You need a clear next step.
Living Algorithms are designed to give you exactly that: treatment direction, dosing, side effects and clinical context in one place.
The question behind every patient consult
Before every clinic visit, there's a familiar mental checklist:
What are the treatment options?
What comes next after progression?
What should I watch for?
How do I explain this to the patient?
Often, you have less than an hour to prepare. And you know the patient will ask about their prognosis and next steps.
Traditional resources don't always help in this moment.
Some tools point you to primary literature. Others give you long guideline documents. Both are valuable, but neither is built for answering a simple, urgent question:
What should I do next for this patient?
Why traditional tools fall short at the bedside
Most oncology tools were not designed for real-time decision making.
Guidelines are comprehensive, but can be difficult to navigate. You start with a broad category, click through multiple layers and eventually land on a recommendation. Along the way, you're parsing long lists of references and trying to map them back to your specific patient.
Literature-based tools are powerful for research and writing. They help you find studies and understand the evidence. But they often stop short of translating that evidence into a clear treatment path for a specific patient.
In practice, this creates a gap:
You know first line for most cancers
But second line and third line decisions are less clear
Real-world nuances are often missing
You end up cross-referencing multiple sources, or asking your colleagues
What's different about Living Algorithms
Living Algorithms are built with one goal in mind: help you make a decision at the point of care.
Instead of starting with a document, they start with the patient.
You can search using the way you actually think:
Biomarker
Stage
Prior lines of therapy
Key clinical features
From there, the algorithm shows you a structured, step-by-step pathway. Not just what the options are, but how to act on them.
What you actually see (and why it matters)
Each step in a Living Algorithm is designed to answer the questions that come up in real practice.
Clear treatment direction
You see first line, second line and beyond in a structured way. This is especially useful when patients come in after multiple prior therapies.
Practical details, not just names
You don't just get a list of drugs. You get:
Dosing and schedules
Real-world adjustments
When to hold or modify treatment
These are the details that often require a separate lookup, or a call to pharmacy.
Side effects and monitoring
You'll also get quick access to:
Common and serious side effects
What to monitor
Labs you actually need, and when you need them
This helps you prepare before the visit and avoid missing something important.
Contraindications and nuance
Not just obvious exclusions, but real-world considerations:
Hepatic or renal impairment
Concomitant medications
Situations where you should proceed with caution
Evidence, without overload
You still see the data that matters:
PFS and OS
Hazard ratios
Key caveats
But it's distilled and tied directly to decisions for the patient in front of you.
Filling the gaps that matter most
In oncology, not every decision is backed by clean, head-to-head data. In the real world, there are "data-free zones" where:
Trials don't answer the exact question
Patient populations don't match your case
Practice varies between institutions and regions
In these moments, expert interpretation becomes critical.
Living Algorithms surface these insights, so you're not left guessing whether your uncertainty comes from your own lack of knowledge or from a lack of data in the field itself.
Built for how clinicians actually think
One of the biggest shifts is moving from a medication-first view to a patient-first view. Instead of asking: "what drugs are available?"
You start with:
Who is this patient?
What stage and biology are we dealing with?
What is the performance level?
What therapies have they already received?
Living Algorithms adapt around those inputs, guiding you toward the most relevant options for your patient case.
From reassurance to confidence
Even when you already know what you plan to do, there's value in quickly confirming it. Many clinicians use decision tools not to discover something new, but to validate that they're on the right path.
Living Algorithms are designed to provide that reassurance:
You see the same pathway you had in mind
You confirm key details quickly
You move into the patient conversation with confidence
Bottom line
At the point of care, speed and clarity matter. You don't have time to piece together answers from multiple sources. You need a tool that brings everything together and helps you act.
Living Algorithms are built for this moment. Not as a replacement for guidelines or literature, but as the layer that connects evidence to real decisions.
Explore Living Algorithms
If you're preparing for a clinic or managing a complex case, try searching for a specific scenario on Open Medicine:
"EGFR-positive lung cancer after progression"
"HR+ HER2-negative breast cancer second line"
See how quickly you can go from question to answer. That's the difference.