Analyzing Patient Stories on Social Media Using Text Analytics.

Abstract

Patients can use social media to describe their healthcare experiences. Several social media platforms, such as the Care Opinion platform, host large volumes of patient stories. However, the large number of these stories and the healthcare system's workload make exploring these stories a difficult task for healthcare providers and administrators. This study uses text mining for analyzing patient stories on the Care Opinion platform and exploring healthcare experiences described in these stories. We collected 367,573 stories, which were posted between September 2005 and September 2019. Topic modeling (Latent Dirichlet Allocation) and sentiment analysis were used to analyze the stories. Sixteen topics were identified representing five aspects of the healthcare experience: communication between patients and providers, quality of clinical services, quality of non-clinical services, human aspects of healthcare experiences, and patient satisfaction. There was also a clear sentiment in 99% of the stories. More than 55% of the stories that describe the patient's request for information, the patient's description of treatment, or the patient's making of an appointment had a negative sentiment, which represents patient dissatisfaction. The study provides insights into the content of patient stories and demonstrates how topic modeling and sentiment analysis can be used to analyze large volumes of patient stories and provide insights into these stories. The findings suggest that these stories are not general social media posts; instead, they describe elements of healthcare experiences that can be helpful for quality improvement.

Supplementary information: The online version contains supplementary material available at 10.1007/s41666-021-00097-5.