Clinical trials vs. real patients: Why clinicians need decision tools for the real world

Clinical trials are designed around carefully selected patients, but in the real world patients rarely match those criteria. Living Algorithms help bridge this gap by translating trial data into practical decisions for the patients that clinicians actually see.

The foundation of modern oncology

Clinical trials are the backbone of oncology. They define:

• Standards of care

• Drug approvals

• Treatment pathways
  

Without them, progress wouldn't be possible. Every guideline, every recommendation, and every major advance starts with trial data.

How trials are designed

To produce clear and reliable results, clinical trials are highly controlled. They use strict inclusion and exclusion criteria to:

• Minimize variability

• Reduce confounding factors

• Ensure patient safety

• Isolate the effect of the treatment
  

In these trials, patients are often:

• Healthier

• More homogeneous

• Carefully monitored

• Treated under ideal conditions
  

This is necessary for science, but creates a gap.

The reality of clinical practice

In the real world, patients are different. They may have:

• Multiple comorbidities

• Organ dysfunction

• Prior toxicities

• Concurrent medications

• Performance status outside trial limits
  

They may not fit neatly into:

• Trial populations

• Guideline categories

• Standard pathways
  

How real-world patients can differ from clinical trial populations

Factor	Clinical Trial Patient	Real-World Patient
Performance Status	ECOG 0–1 (fit, fully active)	ECOG ≥2 is common, with varying levels of frailty
Comorbidities	Limited or excluded	Multiple comorbidities (cardiac, renal, metabolic) are common
Organ Function	Strict renal and hepatic thresholds	Frequently impaired kidney or liver function
Prior Therapies	Controlled and predefined	Multiple prior lines and variable treatment history
Age	Often younger, selected patients	Older population, many ≥75 years
Polypharmacy	Limited concurrent medications	Multiple medications with potential interactions
Special Populations	Often excluded (e.g., brain metastases, autoimmune disease)	Common in routine practice
Monitoring & Adherence	Intensive monitoring, high adherence	Variable follow-up, access, and adherence
Care Environment	Controlled trial setting	Real-world constraints (time, resources, access)
Decision Context	Focus on isolating treatment effect	Balancing efficacy, toxicity, quality of life and patient preferences

Clinical trials tell us what can work under ideal conditions. Real-world care requires adapting that evidence to more complex, less predictable patients.

The mismatch clinicians deal with every day

This creates a common scenario: you know what the trial showed, but you're wondering:

• Does this apply to my patient?

• What if they don’t meet the criteria?

• What are the risks in this situation?

• How would others approach this?
  

These are not edge cases. They're routine.

Why this gap matters

Clinical decisions don't happen in ideal conditions. In the real world, they happen:

• In time-constrained clinics

• With incomplete information

• With patients who don’t fit the mold
  

Guidelines reflect trial data, but they don’t always reflect real-world complexity.

That leaves clinicians to:

• Interpret

• Adapt

• Fill in the gaps
  

Where traditional tools fall short

Most tools are built around the evidence itself. They show:

• Trial results

• Guideline recommendations

• Approved indications
  

But they don't fully address:

• What to do when the patient doesn't match

• How to adjust for real-world factors

• How to think through edge cases
  

The last step is left to the clinician.

The role of clinical judgment

This is where experience matters. Clinicians rely on:

• Pattern recognition

• Prior cases

• Colleague input

• Expert opinion
  

But this knowledge is often:

• Implicit

• Hard to access

• Not systematically captured
  

How Living Algorithms bridge the gap

Living Algorithms are designed to connect trial data to real-world care.

What this looks like in practice

Patient-centered pathways

Instead of starting with trial populations, you start with the patient:

• Biomarkers

• Stage

• Prior treatments

• Performance status
  

Real-world context

Algorithms incorporate:

• Situations where patients fall outside trial criteria

• Practical considerations that affect treatment choice

• Nuances that aren’t captured in studies
  

Integrated decision support

With Living Algorithms, you can see:

• What the evidence says

• How it applies

• What to consider in practice
  

All in one place.

Transparency around limitations

When data is limited or doesn't fully apply, Living Algorithms make this clear. This helps clinicians:

• Recognize uncertainty

• Adjust expectations

• Make more informed decisions
  

From ideal conditions to real decisions

Clinical trials answer: what works under controlled conditions

Clinicians need to answer: what works for this patient

This translation step is critical, and it's where many tools fall short.

Supporting everyday oncology practice

This gap is especially important for:

Community oncologists

Managing a wide range of cancers with limited time and resources.

Trainees

Learning how to apply evidence, not just understand it.

Complex cases

Where patients fall outside the standard pathways.

Bottom line

Clinical trials are essential, but they are only a starting point. In the real world, patients rarely match trial criteria. Your decisions must account for this difference.

Clinicians need tools that go beyond evidence, and help translate it into practice.

Living Algorithms are built for this reality, helping bridge the gap between trials and real-world care.

Try it with your next trial review

The next time you review a trial, ask yourself: does this patient match the population studied?

If not, what adjustments are needed?

That's where decision support matters most.