According to WHO’s definition, palliative care is a method that helps in raising the quality of life of patients and their families facing the problem related to life-threatening illness. This can be done by prevention and relief of suffering by means of initial identification and perfect assessment of physical, and psychosocial treatment of pain.
The University of Vermont’s Vermont Conversation Lab have studied with help of machine learning and natural language processing to better comprehend the difficult conversations which patients and their families have, which could ultimately help healthcare providers improve their near-death communication.
Mr. Robert Gramling, Director of the Lab in UVM’s Larner College of Medicine, who led the study, published in the journal, Patient Education and Counselling, commented, “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.”
They wanted to find out the kinds of conversations that people have around serious ailments to identify the common features they have, if they follow common storylines. To do this, they used the techniques used in the study of fiction, in which machine learning algorithms analyze the language of fiction manuscripts to identify different types of stories.
His team adapted this method to examine 354 transcripts of palliative care conversations collected by the Palliative Care Communication Research Initiative, which involved 231 patients in New York and California.
In conclusion, this information could help healthcare practitioners find out what makes a good conversation about palliative care, and how different kinds of conversations will require different responses. This will result in getting interventions made that are matched to what the conversation indicates the patient needs the most.