Web Scraping Techniques for Surgical Research: A Technical Tutorial With a Worked Example in Publication Data Mining

Abstract

Background

Web scraping—the automated extraction of data from websites—has become an essential technique for researchers seeking to collect large-scale data that would be impractical to gather manually. Surgeon-scientists increasingly encounter publicly available web data relevant to outcomes research, health services analysis, workforce studies, and policy work, yet technical guidance on implementing web scrapers remains limited in the surgical literature.

Methods

This tutorial provides a clinician-oriented technical guide to web scraping for surgical research. We present key concepts including static vs dynamic websites, CSS selectors, browser automation, rate limiting, and ethical considerations. A complete worked example demonstrates the full pipeline by scraping a surgical research group’s publication page (https://www.onetomapanalytics.com) to build a structured bibliometric database.

Results

The worked example successfully extracts structured publication data—including titles, author lists, abstracts, keywords, and PubMed links—from a JavaScript-rendered website, producing an analysis-ready data set. We demonstrate how this pipeline generalizes to other surgical research applications including hospital price transparency data, residency program characteristics, and quality metrics.

Conclusions

Web scraping is a powerful tool for surgeon-scientists when implemented with technical rigor and ethical responsibility. By anchoring the tutorial to a concrete surgical use case and providing a reusable code template, we equip surgical researchers with the foundational knowledge to design, implement, and adapt web scrapers for their own data collection projects.