Classifying unstructured electronic consult messages to understand primary care physician specialty information needs

Abstract:

Objective Electronic consultation (eConsult) content reflects important information about referring clinician needs across an organization, but is challenging to extract. The objective of this work was to develop machine learning models for classifying eConsult questions for question type and question content. Another objective of this work was to investigate the ability to solve this task with constrained expert time resources. Materials and Methods Our data source is the San Francisco Health Network eConsult system, with over 700 000 deidentified questions from the years 2008–2017, from gastroenterology, urology, and neurology specialties. We develop classifiers based on Bidirectional Encoder Representations from Transformers, experimenting with multitask learning to learn when information can be shared across classifiers. We produce learning curves to understand when …