We extract disease taxonomies, treatment protocols, drug interactions, provider directories, and clinical trials from Mayo Clinic. 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 Conditions & Diseases objects from mayoclinic.org. All fields typed and schema-versioned.
"condition_id": "CON-93812", "name": "Atrial Fibrillation", "overview": "Atrial fibrillation (A-fib) is an irregular and often very rapid heart rhythm...", "symptoms": "['Palpitations', 'Shortness of breath', 'Weakness']", "causes": "['High blood pressure', 'Heart attacks', 'Coronary artery disease']", "treatment": "['Blood thinners', 'Beta blockers', 'Cardioversion']", "last_updated": "2025-10-14"
| # | condition_id | name | overview | symptoms | causes | risk_factors |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Drugs & Supplements objects from mayoclinic.org. All fields typed and schema-versioned.
"drug_id": "DRG-4412", "generic_name": "Lisinopril", "brand_names": "['Prinivil', 'Zestril']", "drug_class": "ACE Inhibitors", "indications": "['Hypertension', 'Heart failure']", "side_effects": "['Dry cough', 'Dizziness', 'Headache']", "pregnancy_category": "D"
| # | drug_id | generic_name | brand_names | drug_class | indications | side_effects |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Doctors & Providers objects from mayoclinic.org. All fields typed and schema-versioned.
"npi": "1942381944", "name": "Dr. Sarah Jenkins, M.D.", "specialty": "Cardiology", "locations": "['Rochester, MN']", "education": "['Harvard Medical School', 'Johns Hopkins Hospital']", "certifications": "['American Board of Internal Medicine']", "accepting_new_patients": true
| # | npi | name | specialty | locations | education | certifications |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Clinical Trials objects from mayoclinic.org. All fields typed and schema-versioned.
"nct_id": "NCT04839211", "title": "Efficacy of Novel Beta Blocker in Heart Failure", "status": "Recruiting", "phase": "Phase 3", "conditions": "['Heart Failure', 'Hypertension']", "study_type": "Interventional", "start_date": "2024-01-15"
| # | nct_id | title | status | phase | conditions | study_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Departments & Centers objects from mayoclinic.org. All fields typed and schema-versioned.
"dept_id": "DPT-104", "name": "Department of Cardiovascular Medicine", "sub_specialties": "['Electrophysiology', 'Interventional Cardiology']", "key_treatments": "['Coronary bypass', 'Valve replacement']", "locations": "['Rochester, MN', 'Phoenix, AZ', 'Jacksonville, FL']", "contact_number": "507-284-2511"
| # | dept_id | name | description | sub_specialties | key_treatments | top_doctors |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Mayo Clinic scraper handles complex medical taxonomies, paginated provider directories, and deeply nested condition articles — with strict anti-bot circumvention to guarantee delivery.
Extract the full A-Z condition trees, capturing overview, symptoms, causes, risk factors, and diagnostic criteria as structured text arrays.
Scrape pharmacology data, indications, side-effect matrices, and brand-to-generic mappings across thousands of listed medications.
Parse doctor profiles for credentials, board certifications, clinical interests, and location data across all Mayo Clinic campuses.
Monitor active, recruiting, and completed studies. Extract NCT IDs, phases, eligibility criteria, and principal investigator details.
Capture organisational hierarchies, sub-specialty clinics, contact details, and location coordinates for routing algorithms.
Extract step-by-step care guidelines, surgical procedure descriptions, and post-operative care recommendations.
Map symptom inputs to potential condition outputs to replicate triage logic for internal healthcare applications.
Track updates to clinical guidelines. Our pipelines isolate diffs so you know exactly when a treatment protocol is revised.
Navigate aggressive enterprise firewall protections using residential IPs and TLS fingerprint spoofing.
Brief in. Clean data out.
Select target sections: conditions, drugs, providers, or trials. We map the extraction schema to your downstream requirements.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and anti-bot circumvention for mayoclinic.org.
Schema validation, null-rate checks, and medical text parsing verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Healthcare domains deploy strict bot mitigation and feature complex, unstructured text. Here is how we enforce structure and reliability.
Healthcare domains utilise aggressive Web Application Firewalls (WAF) to block scrapers. We deploy US-based residential ISP proxies, realistic TLS fingerprints, and human-like request delays to maintain continuous access without triggering blocks.
Mayo Clinic condition pages are heavily text-based with inconsistent heading structures. Our parsers use NLP heuristics and fallback XPath chains to cleanly separate 'Symptoms', 'Causes', and 'Treatments' into distinct, queryable arrays.
Provider and clinical trial directories limit results per page and often obscure total counts. We execute recursive traversal algorithms to ensure 100% coverage of the target directory without missing nested profiles.
Medical guidelines update infrequently but critically. We maintain a hash index of last-seen values per article. Subsequent runs only push diffs, providing a clean changelog of medical protocol updates rather than full re-dumps.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, schema drift, and coverage drops. SLA uptime is contractual, not aspirational.
Machine learning teams train medical LLMs, NLP classifiers, and diagnostic models on verified, high-quality clinical text.
Digital health platforms enrich patient portals with authoritative condition overviews and treatment guidelines.
Insurance networks and referral platforms map specialist credentials, locations, and clinical interests to optimise patient routing.
Research organisations monitor active study sites and principal investigators to identify partnership and recruitment opportunities.
Pharmaceutical companies analyse treatment protocols and drug mentions to understand standard-of-care shifts.
Triage applications build underlying logic trees by mapping structured symptom data to potential condition outcomes.
"Mayo Clinic represents the gold standard of publicly accessible medical knowledge — but transforming its vast article network into a structured clinical database requires serious infrastructure."
Extracting healthcare data at scale demands high-precision parsing of nested medical taxonomies and strict circumvention of enterprise bot protection. DataFlirt handles the WAF challenges, schema drift, and deep crawling logic so your engineering team can focus on building clinical applications.
Everything supported by our mayoclinic.org 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, deduplication, and retry logic. Playwright handles JavaScript rendering and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About mayoclinic.org scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available medical information, provider directories, and clinical trial data is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract Protected Health Information (PHI), circumvent patient portals, or violate HIPAA. Clients should consult legal counsel for specific use cases.
We use US-based residential ISP proxies, full Playwright browser sessions with realistic TLS fingerprints, and request timing modelled on human behaviour to bypass WAF protections without triggering blocks.
Medical guidelines update infrequently, but our pipelines can run daily or weekly to detect changes. Provider directories and clinical trials can be synced on a daily cadence to capture new physicians or phase changes.
Yes. We use NLP heuristics and robust selector chains to parse unstructured text into clean arrays, separating symptoms, causes, risk factors, and treatments.
Yes. We extract NCT IDs, study phases, eligibility criteria, and site locations. We can track status changes over time to monitor recruitment progress.
Our smallest packages start at a defined section (e.g., the complete condition A-Z list or the provider directory) with weekly delivery. We price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 100 condition articles or 500 provider profiles as part of the pre-engagement scoping process to validate schema fit.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off extraction of disease taxonomies or a continuous sync of provider directories — we scope, build, and operate the pipeline. Tell us what you need.
