MedReadr: Development and Evaluation of an In-Browser, Rule-Based Natural Language Processing Algorithm to Estimate the Reliability of Consumer Health Articles

INTRODUCTION: The internet is a major source of medical information for patients, yet the quality of online health content remains highly variable. Existing assessment tools are often labor-intensive, invalidated, or limited in scope. We developed and validated MedReadr, an in-browser, rule-based natural language processing (NLP) algorithm that automatically estimates the reliability of consumer health articles for patients and providers. METHODS: Thirty-five consumer medical articles were independently assessed by two reviewers using validated manual scoring systems (QUEST and Sandvik). Interrater reliability was evaluated with Cohen's κ, and metrics with κ > 0.6 were selected for model fitting. MedReadr extracted key features from article text and metadata using predefined NLP rules. A multivariable linear regression model was trained to predict manual reliability scores, with internal validation performed on an independent set of 20 articles. RESULTS: High interrater reliability was achieved across all QUEST and most Sandvik domains (Cohen's κ > 0.6). The MedReadr model demonstrated strong performance, achieving CONCLUSION: MedReadr demonstrates that structural reliability scoring of online health articles can be automated using a transparent, rule-based NLP approach. Applied to English-language articles from mainstream search results on common medical conditions, the tool showed strong agreement with validated manual scoring systems. However, it has only been validated on a narrow scope of content and is not designed to analyze search results for specific questions or detect misinformation. Future research should assess its performance across a broader range of web content and evaluate whether its integration improves patient comprehension, digital health literacy, and clinician-patient communication. Many people use the internet to find medical information, but not all websites provide accurate or trustworthy content. It can be hard for patients and their families to know which articles they can rely on, especially when the information is complicated or presented in a confusing way. We created a tool called MedReadr to help solve this problem. MedReadr is a free browser extension that works in Google Chrome. When a person visits a health-related article online, MedReadr can quickly check the article and give it a reliability score. The score is based on specific features such as whether the article has up-to-date information, includes links to trustworthy sources, uses balanced language, and talks about the uncertainty of treatments. In this study, we tested MedReadr by comparing its scores to those given by human reviewers using two trusted scoring systems. We found that MedReadr’s scores were very similar to the scores given by people, which means it can be a reliable tool for checking medical information online. However, MedReadr is not designed to catch false or harmful information. It also does not evaluate things like how easy the article is to read or how the page is visually designed. Future versions may include more advanced features, but for now, MedReadr can help patients and healthcare providers quickly evaluate the general quality of health articles found on the web.