At ASH23, Andrew Hantel, MD, Dana-Farber, presented his real-time interactive dashboard, which will help identify racial disparities in clinical trial enrollment for acute leukemia patients.
This study that we were doing about um creating a dashboard to help identify disparities in clinical trial enrollment. Um came from the fact that there are non acute leukemia disparities in trial enro enrollment nationally. Um And when we look at a lot of other cancers, we can see that those disparities are largely due to differences in where sites are compared to where populations are. So there are a lot of trials that recruit um in large academic centers, but those academic centers are not necessarily where people from underserved communities live. This is not the case in leukemia and that we get a lot of patients kind of shipped in from all across the state and actually see more than two thirds of everybody diagnosed across the state in our hospitals. And the issue is more getting people onto trials after they get to our institution. And so this study was looking at developing an interactive dashboard that can tell physicians and pis or principal investigators who was enrolled on their studies versus who they actually saw in clinic. And we were able to um create an accurate dashboard after a lot of iterative development of an algorithm to be able to really match patients with their correct oncologist with which studies they were involved in um as well as which clinical group they were being followed by. And so we're able to work with different stakeholders, meaning the clinicians and the different research teams to really develop a dashboard that would be able to provide them real time information across a number of demographics, not only race and ethnicity but interpreter status. Um kind of the different processes of enrollment to be able to say who who is being approached, who is actually being consented to participate and then who is eligible or not for the studies and actually got on. So we're able to do that accurately and then we're able to look at the data themselves and really say, um who were the patients who were not able to get onto the studies? Where were the gaps in the study availability for patients? Um and those different things. And so we did see that um socio-economic affluence like other studies have shown um was a predictor for people being able to enroll. Um But in this case, we actually didn't see that race and ethnicity. Um a predictor, albeit this was likely because of the smaller sample size in the amount of data that we had to look at and not because there aren't actual disparities for, for those groups at the same time. That was a little bit surprising to us. And so we um after the abstract was, was submitted and everything we went and looked back over a larger proportion of data, um we were able to actually see that there were the disparities that we had seen in other studies. And beyond that, it really is a call to action for a couple of different things and that it really gives us not only real time information for demographics, but other things like I mentioned, interpreter status, kind of social determinants of health that we can really intervene on in real time and make sure that people are being approached appropriately, being supported appropriately to be able to participate. Um And so those are some of the takeaways and some of the things that we're really now developing interventions around.
