Natural language processing to describe primary care requests for eConsult specialty care: A simple and practical application.

Abstract

Objective: Free text fields embedded within electronic consultation (eConsult) orders serve as rich sources of descriptive information regarding common uses of this novel telehealth technology. Simple text mining and language processing may efficiently extract key insights that help inform providers and administrators. Methods: Text data from eConsult orders placed within a single academic medical center were extracted from the electronic health record and examined. N-gram frequencies were used to describe the content of eConsult clinical questions and care recommendations. Results: 18,609 eConsults were ordered, with volumes ranging from 12 to 3839 orders across 28 subspecialties. Median character length for the clinical question was 189 and 1393 for specialist response text. Frequency count for top bigram varied greatly by specialty, with a high of 190 ("thyroid nodule") in Endocrinology and a low of 6 ("shoulder pain") in Orthopedics for clinical questions, and a high of 3139 ("ref range") in Endocrinology and a low of 6 ("surgical oncology") in Medical Oncology for specialist response. Discussion: Descriptive word sequences from NLP may provide limited insight into common use cases for eConsult across many subspecialties, though pre-processing was required to generate meaningful results.