Over the past 8 weeks, I have immersed myself in clinical research at the Miller lab, focusing on the safety and tolerability of omadacycline vs. standard of care for bone and joint infections. My work diverged from archetypical lab work, consisting primarily of data analysis, cleaning, and organization. However, I came to embrace it as I realized that my work replaced much of the procedural lab work with problem-solving: finding ways to automate repetitive tasks, asking iterative questions, and doing detective work to trace back the details of what truly happened to each patient throughout the study. Having gone through this whirlwind experience, I am excited to share three experiences and reflections from the past 8 weeks.
My work in the lab involved calculating the frequency of patients who took a particular antibiotic. This seemingly simple task went from hours to weeks as I realized I had used the wrong variables and made incorrect assumptions about how antibiotics were recorded. It was frustrating, but looking back, I realize this is part of the wonder of research. Maybe in coursework we have a 1:1 ratio of effort to output, but in research, I can see how this effort-to-outcome ratio is at least 3:1. That’s just part of the process of building something completely new with meticulous standards. Especially in this kind of translational research, we don’t settle for “good enough” when patients’ lives are at stake.
I had another revelation when I determined the actual duration of treatment for each of the 180 patients. Specifically, I was stunned when Dr. Miller chose to include periods of time when patients were noncompliant and hadn’t actually taken the medication. At first, I thought this meant sacrificing accuracy, but I realized that sometimes consistency trumps accuracy: almost 40% of patients don’t adhere to their prescribed medication, and many don’t report it. So, we use the prescribed treatment period to provide a consistent definition for every patient instead of relying on incomplete, self-reported data. This experience has shifted my perspective on scientific credibility, as data representation is often so complex that there is no perfect way to show it. It has revealed to me how research often involves making compromises while acknowledging potential errors.
A few weeks ago, I attended several research presentations at Harbor-UCLA Medical Center meant for researchers and staff. What surprised me most was the way people asked questions. They didn’t just ask for clarification; they challenged the studies’ methods, questioned the conclusions, and suggested better ways to solve the problems. For example, one presentation looked at how awareness posters could reduce the underprescribing of a certain antibiotic. During the discussion, one researcher suggested that instead of relying on posters, providers could receive a pop-up in the electronic medical record reminding them to consider prescribing the antibiotic when a patient has certain risk factors. Others pointed out that prescription rates appeared artificially high before the intervention, leaving little room for improvement. It’s this rigorous, skeptical culture that solidifies my resolve to be in this field; I want to surround myself with people who aren’t afraid to voice their opinions, question the world around them, and hold themselves and others to high standards of precision.
I’m so thankful for the incredible, thoughtful mentors and peers I have met through this program, as well as for my parents, who drove almost 6 hours each day for the long commute from Irvine to Torrance. Engaging with these people and immersing myself in research as part of the STAR Program has taught me so much about the scientific world and changed how I envision my future path in the medical field.