JMIR Publications

ABSTRACT

Background:

Racial and ethnic diversity in clinical trials for cancer treatment is essential for the development of treatments that are effective for all patients and to identify potential differences in toxicity between different demographics. Mining of social media discussions about clinical trials has been used previously to identify patient barriers of enrollment into clinical trials, however a comprehensive breakdown of sentiments and barriers by various racial and ethnic groups is lacking.

Objective:

The aim of this study was to use an innovative data mining toolset to analyze online conversations about cancer clinical trials and identify and compare conversation topics, barriers and sentiments between different racial and ethnic populations.

Methods:

We analyzed a total of 372,283 online conversations about cancer clinical trials. 179,339 of the discussions (48%) had identifiable race information. Using sophisticated machine learning software and analyses, we were able to identify key sentiments and feelings, topics of interest and barriers to clinical trials across racial groups. Stage of treatment could also be identified in many of the discussions, allowing for unique insight into how the sentiments and challenges of patients change throughout the treatment process for each racial group.

Results:

We observed that only 4% of cancer-related discussions referenced clinical trials. Within these discussions, the most frequently referenced topics of healthcare professional (HCP) interactions, cost of care, fear, anxiety and lack of awareness affected each racial and ethnic group differently throughout the treatment process.

Conclusions:

This information is valuable to identify the ideal content and timing for delivery of clinical trial information and resources for different racial and ethnic groups. It also demonstrates the power of social media data mining in healthcare research.