My Story & Approach
My healthcare journey started in 2002 as a medical assistant at Greenville Technical College, where I quickly discovered that medical coding was where clinical knowledge and precision truly met. I was drawn to the work from day one — the logic of it, the way it connected patient care to data — and I stayed close to the clinical side through phlebotomy and serving as a bilingual liaison for Spanish-speaking patients. College had always been the goal, even when life made it complicated. I built my education one semester at a time while working and raising a family, and I’m proud of that — it taught me how to prioritize, persist, and perform under pressure.
I originally set my sights on medicine, but something shifted during my undergraduate studies at Clemson. My first Bioinformatics course with Dr. Feltus changed how I thought about impact. I never said it out loud at the time, but that class sparked a genuine passion for data — the kind that made me realize I could help more patients by improving the systems behind their care than by treating them one at a time. What I gained from biochemistry wasn’t just domain knowledge. It was a way of thinking: design an experiment, question the assumption, find the signal in the noise. I bring that same instinct to every dataset I work with now — whether it’s claims data in SQL, a HEDIS pipeline in Python, or a risk adjustment model in R. I think of health data analytics as the sidekick superhero — the one the hero actually relies on to complete the mission. The clinician heals. My job is to make sure the data gets out of their way so they can.
My approach to complex problems is a lot like working a puzzle: I collect all the pieces first, then the patterns begin to emerge, then the trends become visible, and eventually the whole picture becomes obvious — and I can see exactly where the empty spaces are and what belongs in them. That process is not something I force. It’s how my brain naturally works. I have genuinely lost track of time in the middle of a difficult dataset. I still think about RNA folding in my spare time — how you can predict the function of a molecule just by knowing its sequence — because that kind of pattern-to-function thinking is the same instinct that drives my analytics work.
During the COVID years, that direction became a concrete goal. I began working toward a Master’s in Computer Science with a focus on building systems that close the gap between what medicine knows and what patients actually receive. What I once understood as a dream to become a physician, I now recognize as something larger: a conviction that the greatest barrier to quality care is not clinical knowledge — it’s the friction embedded in the systems around it. Clinicians already know how to heal. My work is to build the tools that get out of their way, so they can focus on the only thing that should ever matter in a hospital room: the patient in front of them.
Now, with my children grown and independent, I bring that full focus without divided attention. Every system I enter, I approach with curiosity rather than assumptions — because every organization does things differently, and that difference is where the most valuable work usually hides. That’s not what gets me to work every day. It’s what keeps me there.