Renata Yen, PhD, MPH, a health services researcher at the Center for Technology and Behavioral Health and the Department of Biomedical Data Science at Dartmouth’s Geisel School of Medicine, has received an early-career publication award from the International Shared Decision Making (ISDM) Society.
Yen received the award at the ISDM Society’s biennial conference, held recently in Lausanne, Switzerland. The organization is a member-based society that brings together academicians, healthcare practitioners, educators, patient partners, and citizens who are focused on promoting research, training, and practice in person-centered care and shared decision making in healthcare.
“Receiving recognition from such a distinguished group of people, many of whom are leaders in my field, was special,” says Yen. “And then, being able to present data and answer questions about my research in a side oral session was an honor.”
In Yen’s paper, she and her co-authors aimed to describe key, measurable elements of spoken plain language (the use of familiar, clear language) that could be assessed and reported back to clinicians for self-reflection.
“Intuitively, we know that good communication and use of plain language in healthcare encounters improve outcomes, including patients’ emotional health, symptom relief, and functional status. Yet there is limited research on how to measure and report on spoken plain language back to clinicians,” explains Yen, whose research focuses on patient-clinician communication, particularly for patients with lower health literacy or who are socially disadvantaged.
In their study, the researchers analyzed 74 transcripts (randomly selected from a total of about 300 transcripts) that were part of a shared decision making trial across four sites in the U.S. The transcripts were taken from recorded encounters between breast cancer surgeons and patients with early-stage breast cancer.
In trying to understand how clinicians communicate information to their patients, the team sought to find key variables to measure, including how complex a clinician’s language was, how many words they used, and when using medical terms if they followed with an explanation. Also, did the clinicians take turns speaking with the patient? How long did they speak for, and did they pause and ask if the patient was understanding them?
“We found that clinicians had a tendency to use both explained and unexplained medical terms, and that they delivered information using either short turns (one unit of someone speaking) with one topic or long turns with multiple topics—and that these are all measurable elements of spoken plain language,” says Yen.
These findings will support further research on the development of a tool that can be used in medical education and other settings. “This tool could provide direct and specific feedback to improve the plain language practices of clinicians in training and help them improve the care experience for their patients,” she says.
