Practo Scraper [$0.95/1K💰] | Doctor Data & Patient Reviews

Practo Scraper

Build a structured India doctor database from Practo.com — qualifications, years of experience, consultation fee, recommendation percentage, clinic address, geo coordinates, services and medical council registration number — plus a separate patient-stories dataset with full text, diagnosis, treatment and doctor ratings.

Built for healthcare lead generation, hospital and clinic market research, doctor directories, appointment-funnel analytics and competitor benchmarking. HTTP-only: no headless browser, no login, no captcha solver.

What you get

Launch a healthcare lead-gen pipeline

recommendationPercent, consultationFee, clinicName, clinicLocality, clinicCity, latitude, longitude and bookingUrl on every doctor row — enough to score and route leads by location, price band and patient sentiment.

Benchmark fees by specialty and city

specialities[], experienceYears, consultationFee, consultationFeeText and clinicCity let you compute real fee distributions per specialty per city instead of quoting portal averages.

Verify practitioners against the medical register

medicalRegistrationNumber and medicalCouncilName are exposed per doctor. Not every competitor extracts these — it is the field that makes a directory useful for verification and compliance workflows.

Capture patient sentiment, not just ratings

includePatientStories enables the second dataset: patient diagnosis, storyText, consultationDate, treatmentSatisfaction, doctorRecommendationRating and clinicOverallRating. That is what turns a star score into evidence.

Find doctors by name or specialty

startUrls accepts a city specialty listing (https://www.practo.com/bangalore/doctors) or a search URL (https://www.practo.com/search?query=cardiology). Search results expand automatically into full profile pages up to maxDoctorProfiles.

How it works

Practo's public pages are server-rendered and carry all listing data in the initial HTML, so the Actor fetches them over plain HTTP and parses the embedded __NEXT_DATA__ / Apollo cache. No browser is launched, which is why a 39-field doctor profile costs a few HTTP requests rather than a full page-render budget.

The Actor then walks the listing, follows each doctor link, and writes two kinds of item:

• Default dataset — one row per doctor profile.
• Reviews dataset (reviews) — one row per patient story, enabled with includePatientStories.

Every row carries _debug.url so you can trace it back to the exact page it came from, and scrapedAt for change tracking.

Input

Field	Type	Default	Notes
startUrls	string[]	[]	Practo listing or search URLs. Empty falls back to speciality + city.
speciality	string	dentist	Used only when startUrls is empty. Any Practo specialty slug, e.g. cardiology, dermatology, orthopedist.
city	string	bangalore	Used only when startUrls is empty. Practo city slug, e.g. bangalore, delhi, mumbai.
maxCrawledPages	integer	20	Cap on listing pages walked.
maxDoctorProfiles	integer	40	Cap on doctor profiles fetched.
includePatientStories	boolean	false	Enables the reviews dataset.
maxPatientStoriesPerDoctor	integer	10	Cap on patient stories per doctor.
maxReviewsPerProfile	integer	10	Cap on profile-level review fields.
crawlDelayMillis	integer	800	Delay between requests. Raise if you get rate-limited.
proxyConfiguration	object	{ useApifyProxy: false }	Standard Apify proxy picker.
maxRequestsPerRun	integer	500	Hard cap on HTTP requests for the run.

Output

Default dataset — 39 fields, one row per doctor:

name, speciality, specialities[], qualifications, qualifications[], experienceYears, experienceText, consultationFee, consultationFeeText, recommendationPercent, rating, ratingCount, clinicName, clinicLocality, clinicCity, clinicAddress, latitude, longitude, services[], medicalRegistrationNumber, medicalCouncilName, languages[], awards[], isVerified, profileUrl, bookingUrl, photoUrl, about, education[], experience[], hospitalVisits[], patientStoriesCount, hasPatientStories, slug, scrapedAt, _meta

Reviews dataset — 32 fields, one row per patient story:

doctorName, doctorSpeciality, doctorProfileUrl, patientName, patientGender, patientAge, consultationDate, diagnosis, storyTitle, storyText, treatmentExperience, treatmentSatisfaction, doctorRecommendationRating, clinicOverallRating, clinicName, clinicCity, verifiedVisit, helpfulCount, upvotes, downvotes, totalStoriesForDoctor, storyIndex, sourceUrl, scrapedAt, _meta

Field notes that change how you read the data:

• consultationFee is numeric, consultationFeeText is the exact string shown on the page ("₹800" / "Free"). Prefer the numeric one for maths, the text when you need to know it was free.
• recommendationPercent is Practo's own patient-recommendation score (0–100) and is not the star rating. They are different signals; don't merge them.
• medicalRegistrationNumber is null on profiles where Practo does not display it.
• hasPatientStories tells you up front whether a doctor has anything in the second dataset — filter on it before enabling stories, to control cost.

Example input

{
  "speciality": "cardiology",
  "city": "bangalore",
  "maxDoctorProfiles": 60,
  "maxCrawledPages": 15,
  "includePatientStories": true,
  "maxPatientStoriesPerDoctor": 8
}

From an explicit search URL:

{
  "startUrls": ["https://www.practo.com/search?query=dermatology"],
  "maxDoctorProfiles": 100,
  "includePatientStories": false,
  "crawlDelayMillis": 1000
}

Example output

{
  "name": "Dr. Ananya Iyer",
  "speciality": "Cardiology",
  "specialities": ["Cardiology", "Interventional Cardiology"],
  "qualifications": "MBBS, MD (General Medicine), DM (Cardiology)",
  "qualifications": ["MBBS", "MD (General Medicine)", "DM (Cardiology)"],
  "experienceYears": 14,
  "experienceText": "14 yrs experience",
  "consultationFee": 800,
  "consultationFeeText": "₹800",
  "recommendationPercent": 94,
  "rating": 4.8,
  "ratingCount": 126,
  "clinicName": "Aster Cardiac Care",
  "clinicLocality": "Indiranagar",
  "clinicCity": "Bangalore",
  "clinicAddress": "12th Main Road, Indiranagar, Bangalore, Karnataka 560038",
  "latitude": 12.97194,
  "longitude": 77.64013,
  "services": ["Cardiac consultation", "ECG", "Echocardiography", "Angioplasty"],
  "medicalRegistrationNumber": "KMC-74120",
  "medicalCouncilName": "Karnataka Medical Council",
  "languages": ["English", "Kannada", "Tamil"],
  "awards": ["Best Cardiologist - Bangalore 2024"],
  "isVerified": true,
  "profileUrl": "https://www.practo.com/doctors/dr-ananya-iyer-4f2a91c7",
  "bookingUrl": "https://www.practo.com/appointment/4f2a91c7",
  "photoUrl": "https://images.practo.com/doctors/4f2a91c7.jpg",
  "about": "Interventional cardiologist with a focus on preventive cardiology and heart-failure management.",
  "education": [{ "degree": "DM (Cardiology)", "institution": "National Institute of Cardiology" }],
  "experience": [{ "hospital": "Aster Hospital Bangalore", "years": "2016 - present" }],
  "hospitalVisits": [{ "hospital": "Aster Hospital Bangalore", "clinicLocality": "Indiranagar" }],
  "patientStoriesCount": 41,
  "hasPatientStories": true,
  "slug": "dr-ananya-iyer-4f2a91c7",
  "scrapedAt": "2026-10-04T06:41:02.771Z",
  "_meta": { "url": "https://www.practo.com/doctors/dr-ananya-iyer-4f2a91c7" }
}

Integrations & automation

Call it over the Apify API, from Python/JavaScript, the CLI or MCP. Datasets export as JSON, CSV, Excel or XML.

For directory freshness, schedule a weekly run per (speciality, city) pair and upsert on profileUrl: consultation fees and recommendation scores drift, but profile slugs are stable. For hospital market research, keep includePatientStories on and schedule monthly — patient stories accumulate slowly and rarely need daily polling.

Typical pipeline: run on schedule → upsert doctors into Postgres/PostGIS on latitude/longitude → join patient stories on doctorProfileUrl → flag doctors whose consultationFee dropped below your threshold, or whose recommendationPercent fell more than 3 points since last run.

Related Actors

Actor	Why
shahidirfan/practo-doctors-scraper	The busiest Practo Actor, 24.7K users. Good benchmark; check field depth before switching.
automation-lab/practo-scraper	46 fields, HTTP Cheerio, 789 users. Closest feature match.
khadinakbar/practo-doctor-scraper	42 fields with reviews, deep nesting of experience entries.
ahmed_jasarevic/notino-scraper	Same problem, different vertical: Italian cosmetics/retail product data.
ahmed_jasarevic/preply-scraper	The closest pricing/field analogue in this portfolio: professional profiles plus a separate reviews dataset, same $1/1K price point.

FAQ

Why not use the official Practo API?

You can — but it is partner-gated. The Practo API requires approval, a client ID and a partner key, allows roughly 10,000 queries per day at 3 requests/second, and is built for embedding your booking UI in Practo's surface. It is the wrong shape for market research or bulk directory building, where you want the provider side rather than the consumer side.

If you are a healthcare provider integrating booking into your own site, use the official API. If you are a data team, this Actor is the faster path.

How is this priced compared to other Practo scrapers?

Per 1,000 doctors, the Practo scrapers currently listed in the Apify Store sit at roughly $1.00–$10.00 (range compiled from apifystats.com, a third-party pricing aggregator, October 2026). This Actor is $0.95 per 1,000 doctors, at or below the bottom of that range, with a 39-field doctor schema plus a 32-field patient-stories dataset that several comparably-priced alternatives do not offer. Price comparison is a snapshot and providers change it — check the live pricing table on the Actor page.

How do I get patient reviews?

Set includePatientStories: true. Stories go to the separate reviews dataset, keyed by doctorProfileUrl, with diagnosis, storyText, treatmentSatisfaction and per-visit ratings. This roughly doubles the request count per doctor, so use maxPatientStoriesPerDoctor to control it.

Can I filter by recommendation percentage or fee?

Not in the input — Practo does not expose those as URL parameters, so there is nothing to pass through. Filter after the fact on recommendationPercent, consultationFee and ratingCount, which is more flexible anyway.

What happens if startUrls is empty?

The Actor falls back to speciality + city and builds a listing URL itself. Defaults are dentist + bangalore, so an empty input still returns valid data rather than an empty dataset.

Is this safe for patient data?

It reads only what Practo publishes publicly on doctor and patient-story pages, which are rendered for anyone visiting the URL. The patientName field can be a self-displayed first name plus initial. Do not use it to contact patients, and read the disclaimer below before you build anything on top.

SEO Keywords

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For AI Agents & LLM Apps

Returns Practo doctor profiles as flat dataset rows (one per doctor), plus optional patient stories in a second dataset.

Minimal input — smallest input that produces a valid run:

{ "maxDoctorProfiles": 10 }

Typical run — specialty + city with patient stories enabled:

{ "speciality": "cardiology", "city": "bangalore", "maxDoctorProfiles": 60, "includePatientStories": true }

From an explicit search URL (overrides speciality and city):

{ "startUrls": ["https://www.practo.com/search?query=dermatology"], "maxDoctorProfiles": 100 }

Output fields — doctors (39): name, speciality, specialities, qualifications, experienceYears, experienceText, consultationFee, consultationFeeText, recommendationPercent, rating, ratingCount, clinicName, clinicLocality, clinicCity, clinicAddress, latitude, longitude, services, medicalRegistrationNumber, medicalCouncilName, languages, awards, isVerified, profileUrl, bookingUrl, photoUrl, about, education, experience, hospitalVisits, patientStoriesCount, hasPatientStories, slug, scrapedAt, _meta

Output fields — patient stories (32): doctorName, doctorSpeciality, doctorProfileUrl, patientName, patientGender, patientAge, consultationDate, diagnosis, storyTitle, storyText, treatmentExperience, treatmentSatisfaction, doctorRecommendationRating, clinicOverallRating, clinicName, clinicCity, verifiedVisit, helpfulCount, upvotes, downvotes, totalStoriesForDoctor, storyIndex, sourceUrl, scrapedAt, _meta

Behaviours an agent should know

• startUrls wins over speciality and city. Empty startUrls → the Actor builds a listing URL from speciality + city.
• Two datasets. The reviews dataset only fills when includePatientStories: true; otherwise every run has an empty reviews dataset (still returned, still zero items).
• Enabling patient stories adds billed events: $0.95/1K doctor profiles + $1.50/1K patient stories + $2.50/1K if you raise per-profile detail beyond the base. hasPatientStories is the cheap pre-filter.
• recommendationPercent is not the star rating. recommendationPercent (0–100, patient recommendation) and rating (0–5 stars) are independent — never merge them.
• There is no fee or recommendation threshold input; those filters do not exist as URL parameters. Filter in post-processing.
• Billing: $0.00095 per doctor profile, $0.0025 per profile detail, $0.0015 per patient story.

Legal & Compliance

This Actor is an independent third-party tool. It is not affiliated with, endorsed by, or sponsored by Practo, or by any clinic, hospital or medical practitioner listed in its output.

What it actually accesses: publicly visible Practo listing, search, doctor-profile and patient-story pages, over plain HTTP, with no login, no authentication bypass, no captcha solving and no private endpoints. It reads only content that Practo serves to any anonymous visitor.

Personal data: the patient-stories dataset includes self-displayed names (typically a first name plus an initial), age band, gender, consultation date, a diagnosis and a free-text account of treatment. Under GDPR, India's Digital Personal Data Protection Act 2023 and comparable regimes, this is personal — and health-related data is among the most sensitive categories. Practo itself publishes these accounts publicly, but that does not transfer the obligations to you.

You are solely responsible for lawful basis, notice, retention and security obligations for anything you collect. Do not use patient names or stories to contact patients, to re-identify individuals, or for marketing — doing so may breach GDPR/DPDP Act and equivalent law. If you only need doctor-level data, leave includePatientStories off and the dataset stays empty by design.