By Shiv Patel
Collecting data this summer hasn’t seemed like spreadsheets or MRIs all-day. It has been a sense of talk, observation, and reflective thinking after days of being in clinical settings. My data is people: what people say, how people make choices, and what tensions arise when innovation meets reality.
So far, I’ve conducted two semi-structured interviews that really made my capstone approach informed. One was with Dr. David Routman, a research-doctor in Mayo Clinic’s Radiation Oncology Department. His vision is deeply embedded in the culture of cutting-edge science. He explained how AI-powered personalization, genomics, and adaptive radiation methods can help “redefine what’s even possible” in cancer treatment. To him, the holy grail is accuracy in the clinic. The sentence that stuck with me was: If we can “predict toxicity before it occurs, then the treatment becomes powerful and all the more humane.” It was a moving reminder that technology at its finest doesn’t replace care – it complements it.
My conversation with Dr. Juliana Borrás-Osorio, a research fellow in radiation oncology from Colombia, grounded me in a very different perspective. She referred to the sparse equipment in her home country and how most rural patients don’t even receive standard access to radiation therapy, never mind genomic classifiers. She added, “In a lot of places, equity isn’t in innovation but catching up on basics.” It shifted my mental framework of “access” from being solely about new technology to being about foundation-level resources. Her experience helped me see the tension between innovation and implementation with fresh eyes.
Beyond interviews, I’ve also had the opportunity to shadow professionals across a range of departments, including ER, orthopedics, anesthesia, cardiac care, and labor & delivery. These observations gave me an unfiltered view of how care is delivered in real time. In radiation oncology, treatment is highly structured and often protocol-driven. But seeing clinical decision-making in the ER or in delivery convinced me of how often care depends on improvisation, trust, and fast communication. Most striking to me, though, was the implicit teamwork – how even under stressful conditions, care was co-produced across disciplines. That made me realize what implementation science might actually look like in real-world, rural settings where resources are limited and improvisation is an absolute requirement.
Interviews feel personal. Private rooms in which a person is sharing not just what they do, but why. There’s often some pride or some vulnerability that comes up – a mission statement of sorts that colors all the rest. Observations are ambient and layered. You see patterns, behaviors, systemic tension. Interviews show me what a person is passionate about. Observations show me what systems allow for – or not.
When I think about “co-constructing knowledge,” I think about mutuality. It is shifting from “I’m doing research on you” to “we’re learning together.” In my capstone situation, it is letting conversations with patients, clinicians, and community members shape how I share my research – not just referencing them as sources or proof in support. It is understanding that the best answers are the ones built with people, not for people.
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