Real-World Data in Oncology Practice Building Evidence Beyond Clinical Trials

Prof. Nikolay Conev discusses how real-world evidence improves cancer treatment outcomes, support scientific developments, and strengthens evidence-based oncology decision making

Randomized clinical trials remain the gold standard in oncology, but they don’t always capture the complexity of everyday clinical practice.

In our new expert interview series, Sqilline Health speaks with leading oncology specialists about how real-world data (RWD) is transforming cancer care. From understanding treatment effectiveness in diverse populations to informing public health strategy and scientific developments, real-world evidence (RWE) is becoming an essential layer of modern decision-making.

In this edition, Prof. Nikolay Conev, Head of Oncology Clinic at University Hospital St. Marina Varna, shares his perspective on where real-world insights add the greatest value and what it will take for clinicians to fully trust and adopt them.


Clinical trials remain the gold standard, but they rarely reflect everyday practice. Where do you see real-world data adding the most value in oncology today: treatment selection, monitoring, or outcomes assessment?

Real-world data demonstrates how a treatment performs within the actual patient population encountered in daily practice, which often differs significantly from the highly controlled cohorts in clinical trials. Furthermore, real-world data allows for the analysis of “borderline” patients, those who may not strictly meet the inclusion and exclusion criteria of a formal trial. It also enables us to observe the effectiveness of a particular medication in patients with rare conditions or comorbidities that Phase III trials may not have sufficiently covered.


Considering your experience in oncology, what do you see as the most relevant questions to date that real-world data is uniquely suited to answer?

Real-world data provides critical insights into treatment effectiveness within specific geographic regions or countries, where the population often differs from the cohorts included in pivotal registration trials. This is particularly relevant for countries like Bulgaria, which may have a distinct patient profile and different demographic characteristics compared to the populations typically represented in global clinical research.

How do you think the same real-world data can be used to make clinical trials more efficient, for instance in identifying eligible patients or designing more pragmatic protocols that are easier to recruit for?

As previously noted, patient demographics are highly dependent on geographic regions, largely driven by socio-ethnic and behavioural characteristics. These variations lead to either a concentration or a scarcity of specific medical conditions, which can be instrumental in identifying ideal locations to meet particular inclusion criteria for clinical trials. For real-world insights to influence clinical practice, physicians need to trust them.


What standards around data quality, transparency, and analytics are essential for wider adoption among oncologists?

Admittedly, real-world data is not currently the primary driver for establishing clinical practice guidelines; however, it provides answers to questions that clinical trials often cannot address due to insufficient sample sizes. Real-world data can strengthen hypotheses derived from subgroup and post-hoc analyses and, more importantly, generate new hypotheses for further investigation. To put it more informally, it is an essential instrument for medical fine-tuning.

How do you expect real-world data and advanced analytics to reshape oncology care in the near future? What role do you think specialized real-world data platforms like Sqilline Health can play in that transformation?

Real-world data has a scope of applications far beyond what I have already mentioned. It can serve as a cornerstone for evidence-based public health policies derived from ground-level insights. Real-world data is essential for developing comprehensive strategies, ranging from screening initiatives to economic models, that optimize resource allocation by identifying systemic asymmetries and anomalies. It provides a transparent diagnostic of the healthcare system’s actual performance. Furthermore, it allows pharmaceutical companies to refine their strategic planning and enables academic and research institutions to conduct rigorous scientific analysis and data-driven research.

Prof. Nikolay Conev, MD, PhD is a distinguished Bulgarian medical oncologist and academic leader. He is Head of the Clinic of Medical Oncology at University Hospital “St. Marina” – Varna and serves as Professor in the Department of Oncology at the Medical University “Prof. Dr. Paraskev Stoyanov” – Varna. Since 2015, he has authored and co-authored 89 scientific publications in the field of medical oncology.

He is a member of ESMO (European Society for Medical Oncology) and ASCO (American Society of Clinical Oncology) and is an indexed author in J-Stage (Japan Science and Technology Information Aggregator).