Rebecca G Lopez, MD, H. Alejandro Rodriguez, MD, Dustin R Cummings, MD, Andrew S Wright, MD. University of Washington
Background: It has become commonplace for patients to arrive to their appointments self-educated on their medical issues. Popular search engine algorithms are not necessarily based on validity or accuracy of information. The aim of this study is to examine Google search results focused on a top general surgery diagnosis: hernia.
Methods: A Google search was performed in incognito mode, examining the top 20 results for the following phrases: hernia, hernia surgery, hernia mesh, hernia repair, and hernia complications. Results were categorized into the following: medical information (i.e. WebMD, healthcare organization, or society-based pages), non-medical information (i.e. patient-driven community), medical device information, legal advertisement, medical ad, other ads, journal or news article, or links to another search engine. Results were categorized as being high or low-quality information, written by an MD, or being biased against mesh based on qualitative analysis of link content.
Results: Of all searches, only 33% returned medical information and the second most common result was a legal ad, at 18%. 59% of search results were judged to be high-quality and 25% were felt to be biased against use of mesh. A majority of search results for “hernia mesh” were found to be advertisements from law firms. “Hernia mesh” also yielded the highest number of results biased against mesh at 70%. “Hernia repair” yielded the most medical information at 50%.
Conclusions: The results of Internet search engine queries for hernias or hernia repairs are highly variable, subject to bias and low-quality information. Notably, a search for “hernia mesh” resulted in greater than 50% of links by legal firms and links biased against hernia mesh use. Further investigation into patient education will help physicians guide their conversations and counseling of patients.
Presented at the SAGES 2017 Annual Meeting in Houston, TX.
Abstract ID: 88341
Program Number: P012
Presentation Session: iPoster Session (Non CME)
Presentation Type: Poster