Real-World Challenges in Cancer Screening: Understanding Data Sources

In a recent interview with The American Journal of Managed Care^®, Jessica K. Paulus, ScD, senior director of real-world research, Ontada, a business of McKesson, and a trained epidemiologist, addressed challenges in the non–small cell lung cancer space (NSCLC) that include advanced disease at presentation and trends seen in real-world data for these patients.

Here she continues the data discussion, in the setting of screening and diagnosis, by addressing the importance of understanding your data sources and their impact on clinical research.

Transcript

What conditions can be confused for NSCLC, and what should be diagnosis and screening priorities going forward?

The question you just posed in is incredibly important. It’s involving a lot of real benefit-risk trade-offs for screening that affect providers’ likelihood of recommending these or being adherent to sort of clinical guidelines on this, as well as, perhaps most importantly, patient behavior around screening. A lot of these kind of well-recognized trade-offs with screening, such as the false positives, as well as the false negatives, came through in randomized trials of the efficacy of CT screening. However, those investigations, while they were still very large, didn’t necessarily translate to the expected impact in a real-world, or less controlled, setting.

Where I think a lot of this field is now is on really continuing to evaluate and quantify the less dazzling screening realities here, including the false positives and the false negatives, especially in the hands or in the setting of real-world practice.

I also just want to say a caveat that our research here was not specifically designed, nor was it even able to kind of approach some of these questions, for the reason that our data in The US Oncology Network is limited to patients who already have cancer. We don’t have access to patients who are at risk for cancer or who might be eligible for screening for lung cancer, for example. So, we are not able to kind of do the types of analyses that are very well needed, as your question alluded to and my response also addressed. Those studies really need to be assembling large numbers of individuals, who are at risk for lung cancer, from the population.

Some of the population-based research programs or even some of the registry programs in the United States are positioned to answer these questions. Of course, there are cancer prevention epidemiologists who are doing this work. But again, that needs to be using data from a more population-based source.

Where our data at Ontada really shines is that it’s all generated from an oncology-specific EHR [electronic health record] platform called iKnowMed that is specifically designed to support cancer care. As a researcher, it’s a dream come true, because when I’m using the data, I work alongside oncologists who have used iKnowMed in the past and who are very familiar with its interface. So when I have a question about a variable that I’m using, like stage, which is the important one we’re talking about today, an oncologist who’s my colleague at Ontqada will then navigate to the EHR platform and show me exactly how that piece of information is generated at the bedside.

Understanding the source of data and how it originates in the real world is so critical to understanding how fit for purpose it is for a given research activity. All of that considerable advantage, again, is really specific to patients who already have cancer. Less so on the screening side.