Modern internet search analytics and spine: what are patients asking and reading online?


Background:

Google’s People Also Ask feature uses various machine learning algorithms to distill the most frequently asked questions and link users to potential answers. The aim of this study is to investigate the most frequently asked questions related to commonly performed spine surgeries.


Methods:

This is an observational study utilizing Google’s People Also Ask feature. A variety of search terms were entered into Google for anterior cervical discectomy and fusion (ACDF), discectomy, and lumbar fusion. Frequently asked questions and linked websites were extracted. Questions were categorized by topic based on Rothwell’s Classification system, and websites were categorized by type. Pearson’s chi-squared and Student t tests were performed as appropriate.


Results:

A total of 576 unique questions (181 ACDF, 148 discectomy, 309 lumbar fusion) were extracted with 372 unique websites and 177 domains. The most common website types were medical practice (41%), social media (22%), and academic (15%). The most popular question topics were specific activities & restrictions (22%), technical details (23%), and evaluation of surgery (17%). Questions related to technical details were more common in discectomy vs lumbar fusion (33% vs 24%, p=.03) and lumbar fusion vs ACDF (24% vs 14%, p=.01). Questions related to specific activities & restrictions were more common in ACDF vs discectomy (17% vs 8%, p=.02) and ACDF vs lumbar fusion (28% vs 19%, p=.016). Questions related to risks & complications were more common in ACDF vs lumbar fusion (10% vs 4%, p=.01).


Conclusions:

The most frequently asked questions on Google regarding spine surgery are related to technical details and activity restrictions. Surgeons may emphasize these domains in consultations and direct patients to reputable sources of further information. Much of the linked information provided originates from nonacademic and nongovernment sources (72%), with 22% from social media websites.


Keywords:

Anterior cervical discectomy and fusion; Discectomy; Lumbar fusion; Machine learning; Natural language processing; Online health information; Patient education; Search analytics; Social media; Spinal fusion.

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