Why Oncology Real-World Data Is Broken—and How Clinically Relevant Edit Checks Fix It

Pharmacy Times interviews Sarah Spark, MHA MBA, Director of Clinical Data Quality at Ontada, on how clinically relevant edit checks are improving the accuracy and reliability of oncology real-world data to better support clinical decision-making, research, and patient outcomes.

Spark explains that clinically relevant edit checks play a critical role in improving the quality, integrity, and usability of oncology real-world data. She distinguishes standard edit checks—which identify missing, inconsistent, or implausible data—from clinically relevant edit checks that apply additional clinical context and plausibility assessments to oncology-specific variables. These checks are designed to evaluate whether documented clinical information accurately aligns with patient care scenarios, including variables such as resectability status and medication discrepancies. Spark notes that these processes help strengthen quality control by ensuring data are clinically meaningful and reliable for downstream analysis.

During the discussion, Spark identifies several persistent challenges affecting oncology electronic health record–derived databases. Common issues include incomplete staging documentation, missing biomarker and molecular testing results, inconsistent definitions of treatment lines, incomplete progression or response data, and variations in documentation practices across treatment sites. According to Spark, these inconsistencies can reduce both the reliability and comparability of oncology real-world datasets, ultimately impacting research quality and clinical interpretation.

Spark emphasizes that high-quality real-world data can support more confident treatment decisions, improve evaluations of treatment effectiveness and safety outside traditional clinical trials, and help identify best practices for specific oncology patient populations. She explains that the increasing use of personalized oncology approaches has intensified the need for accurate capture of complex biomarker information, genomic testing results, evolving treatment pathways, and rapidly changing clinical guidelines.

She also highlights the important role pharmacists and multidisciplinary care teams play in maintaining accurate oncology data through real-time documentation, medication reconciliation, treatment verification, and consistent capture of key clinical information throughout the patient journey. Looking ahead, Spark predicts oncology data quality standards will become increasingly rigorous as artificial intelligence and advanced abstraction technologies expand, placing greater emphasis on validation, traceability, clinically relevant edit checks, governance, and ongoing quality assurance to ensure datasets remain fit for clinical and regulatory decision-making.