As oncology care becomes increasingly driven by real-world evidence, the quality and consistency of clinical data have become central to how outcomes are measured, compared, and applied in practice. Within this evolving landscape, structured data validation processes—ranging from standard edit checks to more nuanced clinically relevant edit checks—are playing a growing role in ensuring that real-world oncology datasets are both accurate and clinically meaningful.
In an interview with Pharmacy Times, Sarah Spark, MHA, MBA, Director of Clinical Data Quality at Ontada, discusses how different layers of data validation help strengthen oncology research and care. Spark explains the distinction between traditional edit checks, which identify missing, inconsistent, or implausible data, and clinically relevant edit checks, which focus on oncology-specific plausibility measures such as resectability status, staging documentation, and medication or treatment discrepancies. She also highlights common gaps in real-world oncology data, including incomplete biomarker and molecular testing results, variability in treatment line definitions, and inconsistent documentation of progression and response. In addition, Spark outlines how higher-quality data supports more reliable treatment evaluation outside of clinical trials and emphasizes the importance of standardized abstraction processes, ongoing validation, and multidisciplinary collaboration—including pharmacists—in improving data integrity across the patient journey.
Pharmacy Times: For pharmacists and oncology clinicians who may not work directly with data systems, what are “clinically relevant edit checks,” and why are they important in oncology real-world data collection?
Sarah Spark, MHA, MBA: So there are what we call edit checks, and then there are clinically relevant edit checks, if you will. Edit checks are those checks that are programmed to manually review and identify missing, inconsistent, or implausible data within real-world data sources to improve accuracy and reliability. The clinically relevant edit checks are really those that provide that additional plausibility on clinical variables, so things such as resectability, as well as medication discrepancies.
For resectability, it’s capturing unresectable or not documented with surgery also being captured, so it’s actually able to look at and identify, for each study, the specific clinical relevance and plausibility for those specific variables that you’re looking at from a quality control perspective.
Pharmacy Times: What are some of the most common data quality gaps or inconsistencies you see in oncology electronic health record (EHR)-derived databases today?
Spark: Some of the most common gaps probably are staging documentation, missing biomarker and molecular test results, variable treatment line definitions, sometimes incomplete progression and response data, as well as differences in how clinicians actually document across sites. These issues can limit the reliability and comparability of oncology real-world data.
Pharmacy Times: How can improving real-world data quality ultimately impact patient care decisions, treatment evaluation, or health outcomes in oncology?
Spark: Higher-quality real-world data supports more competent treatment decisions, improves evaluation of effectiveness and safety outside clinical trials, and also helps identify best practices for specific patient populations in oncology that can actually translate into more timely, personalized, and evidence-based care.
Pharmacy Times: As oncology therapies become increasingly personalized, what new challenges emerge in maintaining accurate and reliable real-world datasets?
Spark: Personalized oncology increases the need to really capture complex biomarker data, genomic results, evolving treatment pathways, and rapidly changing clinical guidelines. Maintaining data quality absolutely requires continuous education, standardized abstraction processes, and ongoing validation as the oncology landscape evolves.
Pharmacy Times: What role do pharmacists and multidisciplinary care teams play in improving the accuracy and completeness of oncology data documentation?
Spark: Pharmacists and multidisciplinary teams strengthen oncology data quality by documenting care in real time, reconciling medications, confirming treatment details, and ensuring key clinical information is captured consistently. Collaboration is very important for both completeness as well as accuracy across that patient journey.
Pharmacy Times: Looking ahead, how do you see data quality standards evolving as real-world evidence becomes more important for regulatory decisions, value-based care, and clinical research?
Spark: Data quality standards will absolutely continue to become more rigorous, with greater emphasis on validation, traceability, clinically relevant edit checks, and quality assurance as artificial intelligence (AI) and advanced abstraction tools expand. Strong governance is really important to be able to maintain and ensure oncology datasets are accurate, consistent, and fit for decision-making.
I think some of the biggest pieces I want to talk about, especially as it relates to clinically relevant edit checks, are that they actually help impact analysis. They also help improve data integrity, as well as usability. Those are some of the key things when you think about clinically relevant edit checks.
