COVID-19 and the Telehealth Transformation: Insights into MyChart using Natural Language Processing
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COVID-19 has led to the rapid adoption of telehealth strategies in order to maintain continuity of care. As compared to in-person visits, important changes in patient characteristics were seen in telephone and video visits as well as clinician ordering patterns. In addition, MyChart patient portal usage increased dramatically. We present select initial Duke clinic utilization data before and during COVID-19. To better understand the increasing number of unstructured MyChart messages, we apply both unsupervised and supervised machine learning tools to patient-generated messages. Specifically, 1) we utilize dynamic topic modeling to gain insight into message meaning and monthly trends for patients with (+) and (-) COVID and Flu results; 2) we leverage the state-of-the-art machine learning model (Bidirectional Encoder Representations from Transformers or BERT) to construct an automatic message triaging algorithm or classifier that outperforms other baseline methods.
Presented by Jedrek Wosik and Shijing Si. This event is co-hosted by the Duke Center for Computational Thinking (CCT) and Duke+DataScience (+DS).
Before the session, all registrants will receive an e-mail with a link and meeting information.
|Date||Tuesday, September 29th, 2020|
|Time||12:00pm - 1:00pm|