Surfacing the most significant terms for deep content analysis

Trending Terms Analysis

Using our Natural Language Processing (NLP) algorithm we identify the most significant and trending terms in any healthcare conversation. Significance is assessed by a combination of elements including word frequency and level of influence of interlocutors, in other words, word bubble size is determined by algorithm score rather than frequency count.

What vocabulary are patients with diabetes using to describe their experience, and will understanding it lead to better doctor-patient communication?

Our powerful interactive tool gives you insight into what is being discussed and how the conversations evolve over time. The default setting in Symplur Signals compares the 40 most significant words and phrases to those of the previous, corresponding time period, quickly differentiating the unique terms from the common terms.

Dig deeper by isolating a particular stakeholder group’s trending terms and comparing them to those of another stakeholder group—are patients and clinicians sharing the same point of view/are they perceiving things differently?