Machine learning analysis of online patient questions regarding breast reconstruction

Abstract

Background: Social media has become a dominant educational resource for breast reconstruction patients. Rather than passively consuming information, patients interact directly with other users and healthcare professionals. While online information for breast reconstruction has been analyzed previously, a robust analysis of patient questions on online forums has not been conducted. In this study, the authors used a machine learning approach to analyze and categorize online patient questions regarding breast reconstruction.

Methods: Realself.com was accessed and questions pertaining to breast reconstruction were extracted. Data collected included the date of question, poster's location, question header, question text, and available tags. Questions were analyzed and categorized by two independent reviewers.

Results: 522 preoperative questions were analyzed. Geographic analysis is displayed in Figure 1. Questions were often asked in the pre-mastectomy period (38.3%); however, patients with tissue expanders currently in place made up 28.5%. Questions were often related to reconstructive methods (23.2%), implant selection (19.5%), and tissue expander concerns (16.7%). Questions asked in the post-lumpectomy period were significantly more likely to be related to insurance/cost and reconstructive candidacy (p < 0.01). The "Top 6″ patient questions were determined by machine learning analysis, and the most common of which was "Can I get good results going direct to implant after mastectomy?"

Conclusions: Analysis of online questions provides valuable insights and may help inform our educational approach toward our breast reconstruction patients. Our findings suggest that questions are common throughout the reconstructive process and do not end after the initial consultation. Patients most often want more information on the reconstructive options, implant selection, and the tissue expansion process.

Keywords: Breast reconstruction; Machine learning; Patient questions regarding breast reconstruction; RealSelf; Social media.