Enhancing Search Engine Optimization in Healthcare and Clinical Domains with Natural Language Processing and Graph Techniques

Search Engine Optimization (SEO) is the art of refining a website to enhance its visibility in search engine results, capturing the attention of both potential and existing customers. At Kaiser Permanente Digital, our unwavering commitment is to provide individuals with pertinent and precise health-related information. In this study, our primary objective is to elevate the rankings of KP.org webpages. To attain this goal, we leverage data from a third-party platform and harness cutting-edge Natural Language Processing (NLP) techniques, including the powerful large language model BERT. Our NLP arsenal encompasses diverse techniques, such as clustering and topic modeling, designed to extract invaluable insights from our data. Moreover, we complement our findings with practical examples and compelling visualizations tailored to the clinical and healthcare domain. Additionally, we conduct thorough graph analysis, employing methods like node2vec, to identify pages with closely related content within our domain, addressing the issue of keyword cannibalization and content competition for ranking. In this paper, we present our innovative solutions in a visually intuitive manner, showcasing how these approaches not only optimize our content effectively but also ensure strategic and non-redundant keyword utilization across our website.