Addressing Racial and Ethnic Disparities in AML Clinical Trials | Andrew Hantel, MD | ASH 2023

Hi, I'm Andrew Antel, a medical oncologist and leukemia researcher at Dana-Farber. And I'm going to be talking to you a little bit about our research on developing an interactive dashboard for oncologists to be able to understand the diversity of their patients who enroll on clinical trials. So we know that there are large disparities by race, by ethnicity, by age, sex, and socioeconomic status in terms of who is able to participate in a clinical trial across the United States. Specifically for acute leukemia, it's a little bit different than most solid tumors in that most solid tumor disparities in enrollment in clinical trials are because the sites that the clinical trials are at usually in different spots than the patients from those marginalized groups live. In leukemia though, most patients, because the diseases are not as common, do get shipped or transported into one of the major academic centers because the treatment needs to happen quickly, usually happens on the inpatient side. And so we've seen that actually the disparity in terms of who's enrolled is between who hits the door and who gets enrolled rather than who can actually get to the site. And so this research was trying to develop a dashboard to support oncologists understanding who they've been able to enroll on trials and who has not been able to enroll. And so we were able to kind of accurately put together a dashboard that assigned patients to the correct physician, to the correct enrollment protocols that they had participated in, and to be able to follow them over time. And so we were able to do that across a number of different metrics, as I mentioned, race, ethnicity, socioeconomic status, and those kinds of things, but also really the processes of enrollment and where the differences were. So who was actually approached to participate in the research, who consented to the research, who was eligible, and then who actually went into the studies and kind of seeing how those differences played out over time. And so the dashboard that we created can really do that accurately and in real time for physicians and kind of look at both their patients, look at the whole clinical group, look at the whole set of clinical trials that they have there, and really look kind of multi-dimensionally. And we're able to see amongst our data that there were disparities by race and ethnicity, by socioeconomic status, by really where patients lived and kind of how far they lived from one of our centers, as well as by the type of leukemia that they had and kind of understanding how all those different factors played into who was able to participate in our trials. And while this isn't kind of an intervention study into itself, it really sets up our institution and is applicable to other institutions in that it's just kind of a data, programmed data set that you can kind of install at different institutions and really use as a way to kind of understand and benchmark other interventions that you want to do to try and recruit patients from these marginalized populations. Because this can give you real time data, you don't need to go in and kind of look at it and do each time, do new analyses. It's kind of a plug and play thing that you can see how efficacious or how effective these different interventions that you want to do are. And so we're able to kind of now hopefully work with other institutions to really install this at their places and really understand how it affects providers and researchers' ability to understand which populations are being left out and kind of how we can include them in our research moving forward.