New York: Researchers at the University of Vermont have used machine learning and natural language processing (NLP) to better understand conversations about death, which could eventually help doctors improve their end-of-life communication.
Some of the most important, and difficult, conversations in healthcare are the ones that happen amid serious and life-threatening illnesses.
Discussions of the treatment options and prognoses in these settings are a delicate balance for doctors and nurses who are dealing with people at their most vulnerable point and may not fully understand what the future holds.
“We want to understand this complex thing called a conversation. Our major goal is to scale up the measurement of conversations so we can re-engineer the healthcare system to communicate better,” said Robert Gramling, director of the Vermont Conversation Lab in the study published in the journal Patient Education and Counselling.
Gramling and his colleagues used machine learning algorithms to analyze 354 transcripts of palliative care conversations collected by the Palliative Care Communication Research Initiative, involving 231 patients.
They broke each conversation into 10 parts with an equal number of words in each, and examined how the frequency and distribution of words referring to time, illness terminology, sentiment and words indicating possibility and desirability changed between each decile.
“We picked up some strong signals,” said Gramling.
Conversations tended to progress from talking about the past to talking about the future, and from sadder to happier sentiments. “There was quite a range, they went from pretty sad to pretty happy,” Gramling added.
The consistent results across multiple conversations show just how much people make meaning out of stories in healthcare.
“What we found supports the importance of narrative in medicine,” he said.
That knowledge could eventually help healthcare practitioners understand what makes a “good” conversation about palliative care, and how different kinds of conversations might require different responses.
That could help create interventions that are matched to what the conversation indicates the patient needs the most.
A deeper understanding of these conversations, which are often freighted with emotion and uncertainty, will also help reveal what aspects or behaviors associated with these conversations are more valuable for patients and families.