{"doi":"10.1159/000548163","title":"MedReadr: Development and Evaluation of an In-Browser, Rule-Based Natural Language Processing Algorithm to Estimate the Reliability of Consumer Health Articles","abstract":"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: < 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. 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.","journal":"Biomedicine Hub","year":2025,"id":577471,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9531,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1487019,"name":"Autumn Kim","orcid":null,"position":1,"is_corresponding":false},{"id":1342741,"name":"Nikit Venishetty","orcid":"0000-0002-2289-3079","position":2,"is_corresponding":false},{"id":1486582,"name":"Alia Codelia‐Anjum","orcid":"0000-0003-4301-1001","position":3,"is_corresponding":false},{"id":1355977,"name":"Dean Elterman","orcid":"0000-0003-1507-7783","position":4,"is_corresponding":false},{"id":6297,"name":"Naeem Bhojani","orcid":"0000-0003-2679-2635","position":5,"is_corresponding":false},{"id":1487020,"name":"Kevin C. Zorn","orcid":null,"position":6,"is_corresponding":false},{"id":428558,"name":"Adithya Balasubramanian","orcid":"0000-0002-7912-8896","position":7,"is_corresponding":false},{"id":72212,"name":"Andrew J. Vickers","orcid":"0000-0003-1525-6503","position":8,"is_corresponding":false},{"id":1486583,"name":"Bilal Chughtai","orcid":"0000-0002-0515-2578","position":9,"is_corresponding":false},{"id":853433,"name":"Joshua Winograd","orcid":"0000-0002-7386-4323","position":0,"is_corresponding":true}],"reference_count":12,"raw_metadata":null,"created_at":"2026-07-19T02:58:04.622308Z","pmid":"41064735","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}