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.
This data allows plastic surgeons to better tailor their preoperative consultations to address common concerns, set realistic expectations, and improve overall satisfaction.
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.
By utilizing the data that social media websites, like Realself.com, provide, plastic surgeons can better understand common patient concerns. This data aids in optimizing the preoperative consultation process to address the common concerns, recalibrate unrealistic expectations, and improve overall satisfaction.