Improving Consumer Experience for Medical Information Using Text Analytics

Abstract

Detecting language nuances from unstructured data could be the difference in serving up the right Google search results or using unsolicited social media chatter to tap into unexplored customer behavior (patients and HCPs). However, as an established science, there is a slow adoption of NLP and Text Analytics in healthcare sector for analysis of unstructured textual data originating from customer interactions. One of the key areas of our exploration is the Medical Information function within our organization. We receive a significant amount of medical information inquires in the form of unstructured data through multiple communication channels. The current system of gathering insights takes significant time and effort – as information must be manually tagged and classified limiting the ability to drive insights and trends efficiently and in a timely manner. These limitations mean subject matter experts must spend time manually deducing insights and aligning with medical affairs – time that could be better spent elsewhere. Therefore, this article presents an approach using NLP & Text Analytics to generate valuable insights from unstructured medical information inquiries. The system automatically extracts key phrases, medical terms, themes, sentiments as well as leverages unsupervised statistical modeling for two-level categorization of inquiries. Results of NLP when analyzed with the aid of visual analytics tool highlighted non-obvious insights indicating the value it can generate to influence product strategies.