What Do Our Patients Truly Want? Conjoint Analysis of an Aesthetic Plastic Surgery Practice Using Internet Crowdsourcing

Abstract

Background: What do patients want when looking for an aesthetic surgeon? When faced with attributes like reputation, years in practice, testimonials, photos, and pricing, which is more valuable? Moreover, are attributes procedure-specific? Currently, inadequate evidence exists on which attributes are most important to patients, and to our knowledge, none on procedure-specific preferences.

Objectives: First, to determine the most important attributes to breast augmentation, combined breast/abdominal surgery, and facelift patients using conjoint analysis. Second, to test the conjoint using an internet crowdsourcing service (Amazon Mechanical Turk [MTurk]).

Methods: Anonymous university members were asked, via mass electronic survey, to pick a surgeon for facelift surgery based on five attributes. Attribute importance and preference was calculated. Once pre-tested, the facelift, breast augmentation and combined breast/abdominal surgery surveys were administered worldwide to MTurk.

Results: The university facelift cohort valued testimonials (33.9%) as the most important, followed by photos (31.6%), reputation (18.2%), pricing (14.4%), and practice years (1.9%). MTurk breast augmentation participants valued photos (35.3%), then testimonials (33.9%), reputation (15.7%), pricing (12.2%), and practice years (3%). MTurk combined breast/abdominal surgery and facelift participants valued testimonials (38.3% and 38.1%, respectively), then photos (27.9%, 29.4%), reputation (17.5%, 15.8%), pricing (13.9%, 13.9%), practice years (2.4%, 2.8%).

Conclusions: Breast augmentation patients placed higher importance on photos; combined breast/abdominal surgery and facelift patients valued testimonials. Conjoint analysis has had limited application in plastic surgery. To our knowledge, internet crowdsourcing is a novel participant recruitment method in plastic surgery. Its unique benefits include broad, diverse and anonymous participant pools, low-cost, rapid data collection, and high completion rate.