By Rachel Lane
Posted July 10, 2018
"Five mikes." I repeated the words to myself, closing my eyes and hoping a clear picture of "five mikes" would materialize from the darkness.
By the grace of one benevolent scientist, I was finally working in a lab. A month before, I had been hustling hospital hallways writing nutritional care plans for patients. The only thing I knew about the research world was that I desperately wanted to be a part of it -- to scratch the questions itching my mind.
I had to answer those burning questions, but first, I needed to figure out what "mikes" were. I had been talking with a scientist about an assay (even "assay" was a new word to me at that time!) I wanted to perform, and he told me to add "five mikes" of reagent. My own pride kept me from clarifying what he meant by "mike." Microliter? Micromolar? Microgram? The information had been casually tossed into my hand without any preceding contextual questions, so I assumed I should know exactly what a "mike" was.
I asked another scientist what "mike" meant, but he didn't know either.
Eventually, I determined that "mike" was short for "microliter," which helped me...in no way at all. Now, I had to return to the scientist with more questions: what is the initial concentration of the reagent? What is the final reaction volume/reagent concentration? Each question provided helpful information, but the experimental timeline would have been expedited if the scientist had initially said "The final reagent concentration should be 5 micromolar, Rachel."
Critical information hides behind assumptions
The final concentration was the useful, applicable information that I needed. However, the scientist and I both made critical assumptions. I assumed that the information he provided was basic knowledge, not colloquial shorthand, and the scientist made assumptions about my initial level of knowledge, my experimental system, and the commonality of "mike" as an abbreviation for microliter.
Scientists and clinicians speak different languages
My own inexperience undoubtedly contributed to this specific miscommunication, but I encountered multiple interactions like this throughout my graduate career and observed similar exchanges between proficient scientists. After experiencing and observing the awkwardness and inefficiency of these conversations, I learned how to ask the right question to extract the information I needed.
These interactions reveal why translating science into medical applications can be so difficult: clinicians and scientists have just enough in common and just enough crossover that many assumptions are made by these well intentioned, competent experts. Each of these vocations has a distinct language, fueled by unique training environments and realities. Scientists are challenged to explore deep into the unknown, and unfortunately, this practice may cause scientists to become disconnected from what not only the general public but also their peers know.
Just start talking
Fortunately, collaboration among scientists is becoming more valued, but physicians and scientists must also start conversing on a regular basis.(1,2) Scientists must learn to ask questions that elicit necessary information from clinicians. The conversation may be messy at first, but it's the only place to start. Scientists need to hear the unresolved problems and suboptimal solutions that clinicians encounter. From this information, scientists can create new solutions or repurpose known technology to satisfy clinical deficits. Communicating these efforts and breakthroughs will empower clinicians to advocate for research - testifying that basic science is a valuable source of beneficial interventions. Just like I did, scientists must learn to ask the important questions that improve their research.
1. Mediati D. Science is the name but collaboration is the game | PLOS ECR Community. PLOS Blogs: Early Career Research Community. http://blogs.plos.org/thestudentblog/2017/04/14/science-is-the-name-but-collaboration-is-the-game/. Published 2017. Accessed July 10, 2018.
2. Hsiehchen D, Espinoza M, Hsieh A. Multinational teams and diseconomies of scale in collaborative research. Sci Adv. 2015;1(8):e1500211-e1500211. doi:10.1126/sciadv.1500211
Image created from the following Noun Project pictures: long hair by Kirby Wu; drop by Tami Nova; sand by Ana MarÃa Lora Macias; and Human by Alex Muravev.