We extract condition guides, drug information, symptom data, and provider directories from everydayhealth.com. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Condition Guides objects from everydayhealth.com. All fields typed and schema-versioned.
"condition_name": "Type 2 Diabetes", "overview": "Type 2 diabetes is a chronic condition...", "symptoms": "['Increased thirst', 'Frequent urination', 'Fatigue']", "diagnosis": "A1C test, Fasting blood sugar test", "author": "Dr. Jane Doe", "reviewer": "Dr. John Smith", "date_updated": "2023-11-14", "url": "https://www.everydayhealth.com/type-2-diabetes/guide/"
| # | url | condition_name | overview | symptoms | causes | diagnosis |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Drug Monographs objects from everydayhealth.com. All fields typed and schema-versioned.
"generic_name": "Metformin", "brand_names": "['Glucophage', 'Fortamet', 'Glumetza']", "drug_class": "Biguanides", "dosage_forms": "['Oral tablet', 'Oral solution']", "side_effects": "['Nausea', 'Vomiting', 'Diarrhoea']", "warnings": "Lactic acidosis risk", "pregnancy_category": "Category B", "manufacturer": "Bristol-Myers Squibb"
| # | generic_name | brand_names | drug_class | dosage_forms | side_effects | interactions |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Provider Profiles objects from everydayhealth.com. All fields typed and schema-versioned.
"npi": "1234567890", "name": "Dr. Sarah Jenkins", "specialty": "Endocrinology", "hospital_affiliation": "Mount Sinai Hospital", "address": "1428 Elm St, New York, NY", "accepted_insurance": "['Medicare', 'Blue Cross', 'Aetna']", "board_certifications": "['Internal Medicine', 'Endocrinology']", "years_experience": 14
| # | npi | name | specialty | hospital_affiliation | address | phone |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Medical Articles objects from everydayhealth.com. All fields typed and schema-versioned.
"title": "10 Superfoods for Heart Health", "category": "Diet & Nutrition", "author": "Emily Chen, RD", "reviewer": "Dr. Alan Grant", "publication_date": "2023-09-21", "tags": "['Heart Health', 'Diet', 'Superfoods']", "reading_time": "5 min", "url": "https://www.everydayhealth.com/diet-nutrition/heart-health-superfoods/"
| # | title | category | author | reviewer | publication_date | content_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Symptom Data objects from everydayhealth.com. All fields typed and schema-versioned.
"symptom_name": "Chronic Cough", "related_conditions": "['Asthma', 'GERD', 'Bronchitis']", "severity_flags": "['Coughing up blood', 'Shortness of breath']", "when_to_see_doctor": "If cough lasts more than 8 weeks", "home_remedies": "['Honey', 'Humidifier', 'Hydration']", "diagnostic_tests": "['Chest X-ray', 'Spirometry']", "age_group": "Adults", "last_reviewed": "2023-08-10"
| # | symptom_name | related_conditions | severity_flags | when_to_see_doctor | home_remedies | diagnostic_tests |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our everydayhealth.com scraper is engineered to parse complex medical taxonomies, drug monographs, and provider directories. We handle the structural variability of legacy content and modern SPA pages.
Extract nested condition hierarchies, symptom lists, and treatment protocols with strict schema validation.
Capture dosage, side effects, interactions, and FDA warnings from structured drug databases.
Extract NPI, specialties, affiliations, and contact details from paginated doctor search results.
Capture full article text, author credentials, reviewer details, and publication dates for clinical news.
Map symptom-to-condition relationships and severity flags from interactive checker tools.
Track medical updates by diffing content body and revision dates across pipeline runs.
Capture URLs for anatomical diagrams, condition imagery, and instructional videos embedded in content.
Bypass rate limits and CAPTCHAs on everydayhealth.com using residential proxies and TLS fingerprinting.
Deliver clean, tag-stripped text blocks optimised for ingestion into LLM training pipelines.
Brief in. Clean data out.
Provide categories, drug classes, or provider search parameters. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and taxonomy parsers for everydayhealth.com.
Schema validation, null-rate checks, and content completeness verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Medical publishers use deeply nested HTML and varied templates for different content types. Here is how we maintain data integrity.
Condition guides on everydayhealth.com often use different DOM layouts depending on the publication year. We use multi-selector fallback chains and NLP-based heading recognition to map variable sections (Symptoms, Causes, Treatment) into a consistent schema.
Provider directories and article feeds use JavaScript-based infinite scroll or dynamic pagination. We run Playwright to intercept API responses or trigger scroll events, ensuring total capture of large listing sets without missing records.
Medical content requires strict versioning. We maintain hash indexes of article text and drug monographs, emitting diffs only when authors, reviewers, or clinical details change.
Clinical articles contain dense citation lists. We parse these reference blocks into structured arrays, capturing DOI links, journal titles, and publication years for downstream validation.
Scraping thousands of provider profiles triggers IP bans. We distribute requests across US-based residential proxy pools, injecting realistic delays to maintain high throughput without alerting firewall rules.
AI companies ingest peer-reviewed condition guides and symptom data to train medical chatbots and diagnostic models.
Pharmacies and health tech platforms aggregate side effects, interactions, and dosage forms to enrich internal drug catalogues.
Insurance and telehealth companies scrape doctor directories to verify NPIs, specialties, and competitive network density.
Health portals syndicate medical news and clinical reviews, using our diff pipelines to stay updated.
Researchers track publication frequency of specific condition articles as a proxy for public health trends.
Publishers analyse everydayhealth.com content velocity, author output, and keyword targeting to inform editorial strategy.
"Everydayhealth.com holds a massive corpus of peer-reviewed condition and drug data — but it remains locked in HTML until you build the pipeline."
Extracting medical taxonomy requires more than standard web requests. You need structured parsing for complex drug monographs, JavaScript rendering for provider directories, and diffing engines to track critical clinical updates. DataFlirt handles the infrastructure so your data science teams can focus on NLP and analysis.
Everything supported by our everydayhealth.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and deduplication. Playwright handles JavaScript execution for dynamic directories. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required to evade IP bans.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. State is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About everydayhealth.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available clinical information is generally permissible. DataFlirt extracts only public, non-authenticated articles, drug data, and provider directories. We do not extract personal health information (PHI) or bypass authentication walls.
We use multi-layer fallback chains and heuristic heading detection. If an article uses a legacy template, our CSS/XPath selectors fall back to text-pattern matching to extract the correct sections.
Yes. We capture the 'last reviewed' or 'updated' timestamps and maintain a hash of the content body. We emit a diff record when a change is detected.
Yes. We traverse the directory using location and specialty parameters, handling the JavaScript pagination to extract complete lists of doctors and facilities.
JSON, CSV, Parquet, and direct integrations with S3, BigQuery, and Snowflake. For LLM training, we provide clean JSON with stripped HTML.
We typically start with a defined extraction scope, such as a specific drug class or condition category, with monthly or weekly refresh cadences.
Yes. We provide a sample extraction of up to 100 articles or provider profiles during scoping to validate the schema.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full export of drug monographs or continuous updates of medical articles — we scope, build, and operate the pipeline. Tell us what you need.
