MedReadr: Development and Evaluation of an In-Browser, Rule-Based Natural Language Processing Algorithm to Estimate...

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.

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.

High interrater reliability was achieved across all QUEST and most Sandvik domains (Cohen's κ > 0.6). The MedReadr model demonstrated strong performance, achieving R ² = 0.90 and RMSE = 0.05 on the development set and R ² = 0.83 and RMSE = 0.07 on the validation set. All model coefficients were statistically significant (p < 0.05). Key predictive features included currency and reference scores, sentiment polarity, engagement content, and the frequency of provider contact, intervention endorsement, intervention mechanism, and intervention uncertainty phrases.

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.