A machine learning analysis of patient concerns regarding mastopexy.

[BACKGROUND] Social media plays an important role in connecting patients and plastic surgeons. We utilized patient inquiries regarding mastopexy from an online social media site to determine the most prevalent patient concerns, while employing a machine-learning algorithm to generate the questions representative of the dataset.

[OBJECTIVE] This data allow plastic surgeons to better tailor their preoperative consultations to address common concerns, set realistic expectations, and improve overall satisfaction.

[METHODS] A total of 2,011 inquiries from the mastopexy section of Realself.com were obtained using an open-source web crawler. Each inquiry was manually categorized as preoperative or postoperative and classified into subcategories based upon the free text entry. Lastly, questions were analyzed using machine learning to determine ten questions most representative of the inquiry pool.

[RESULTS] Of the 2,011 inquiries analyzed, 52.91% were preoperative and 47.09% were postoperative. Most preoperative questions asked about procedure eligibility (309, 29.04%), surgical techniques and logistics (260, 24.44%), and the best type of breast lift for the user (259, 24.34%). Among postoperative questions, questions regarding appearance were the most common (491, 51.85%), followed by symptoms after surgery (197, 19.75%) and behavior allowed/disallowed (145, 15.31%). Appearance was further subcategorized with the most common categories being appearance of the nipple (98, 19.86%), skin discoloration (88, 17.92%), and scarring (74, 15.07%).

[CONCLUSION] By utilizing the data that social media websites, like Realself.com, provide, plastic surgeons can better understand common patient concerns. This data aid in optimizing the preoperative consultation process to address the common concerns, recalibrate unrealistic expectations, and improve overall satisfaction.