Inpharmus

In clinical oncology, the key barrier is not the lack of data, but its interpretation.

Healthcare systems are increasingly “data rich, information poor.” High statistical reliability can mask clinicalvariability. The same evidence can lead to different policy decisions. And genomic data often remains a grayzone in practice.

The focus must shift from generating more data to standardizing how we interpret it. Real-world evidenceand multidisciplinary alignment are critical to turning data into meaningful outcomes.

Strong clinical evidence is fundamental. It defines what is possible under controlled conditions. Yet, what happens in practice isshaped by a different set of realities.

In clinical settings, adoption is rarely driven by effectiveness alone. It is influenced by how well an approach fits into existing workflows, howeasily teams can coordinate around it, and how seamlessly it integrates into patient pathways.

Operational alignment, timing, resource availability, and day-to-day practicality often determine whether evidence translates into impact.

This is why some approaches, despite compelling efficacy data, struggle to find their place in routine care. Others, with balanced performanceand high feasibility, are more readily adopted and sustained.

The real question may not be effectiveness or feasibility, but how well these two dimensions align.

Clinical research generates evidence, but it also shows where evidence is still limited.

These gaps are part of the process.

They guide new studies, support better decisions, and contribute to improving patient care.