Jameda Scraper: How to Get German Doctor Data at Scale

A Jameda scraper turns jameda.de — Germany's largest doctor-rating and booking platform — into a spreadsheet: one row per doctor or practice, with name, specialty, address, coordinates, rating, review count, verification badges and online-booking flags, and optionally the practice website, a public email and every patient review. Instead of opening profiles one at a time, you pick a city and a specialty, run the job, and get thousands of structured records for territory planning, prospecting or market research.

The Livescraper Jameda Scraper does exactly that, without code or proxies. This guide covers what a Jameda profile actually carries, every column the export returns, how to scope a search so you only pay for rows you need, the use cases where German doctor data earns its keep, and — because this is Germany — the outreach rules you have to respect before you contact anyone on the list.

What is Jameda, and why is its doctor data useful?

Jameda is where German patients go to find a doctor, read what other patients thought, and increasingly book an appointment directly. A profile is a public-facing record that the practice has every incentive to keep current: the specialty it wants to be found for, the address patients should go to, its rating and reviews, whether the profile is verified, and whether patients can book online.

For anyone who sells to, recruits from, or studies the German outpatient sector, that combination is hard to get elsewhere. Business registers do not tell you a practice's specialty. Generic directories rarely tell you whether a practice already uses online booking. Google Maps covers practices well but organises them by its own categories rather than by medical specialty. Jameda is organised around the thing healthcare teams actually segment on — what kind of doctor is this — which is why German doctor data — Ärzte-Daten organised by Fachrichtung — from Jameda is such a clean starting point.

What data does the Jameda scraper return?

Each run returns 29 core columns per search result, pulled directly from Jameda's search and profile pages. No website crawling is needed for these. They group naturally into seven blocks.

A few of these deserve a closer look. result_id is the column that ties everything together — it is how a review row links back to its doctor. The four specialty columns give you both a readable label and IDs, so you can filter consistently even when labels vary. has_calendar and has_calendar_slots are the online-booking signals, which matter enormously if you sell practice software. And result_position records where a profile appeared in the Jameda results for your search, which is useful when you are studying visibility rather than just presence.

Optional: website and email enrichment

Switch on profile + email enrichment and two columns are appended: website, the URL from the doctor's profile if there is one, and email, a public business email parsed from that linked website. The scraper visits each linked site to find it, so email is blank when the practice has no website, publishes no address, or the site blocks the visit. Expect a partial fill rate and plan for it rather than treating blanks as errors.

Optional: the separate reviews file

Toggle the reviews enrichment and each task produces a second file containing every patient review for every profile in the main result set, one row per review. Each row carries result_id (linking back to the doctor), review_text, review_date, review_rating and reviewer_alias when one is present. Keeping reviews in their own file is the right shape for analysis: you can count, date-bucket and sentiment-score reviews without duplicating doctor columns on every line, then join on result_id when you need both.

What is not in the export

Plan your workflow around the columns above. The core column list has no dedicated phone field, so if your team works the phone, take the practice's number from its Google Maps listing — more on that combination below. Opening hours are likewise better sourced from Maps.

How to scrape Jameda: a step-by-step run

1. Sign in and open the Jameda Scraper in the Livescraper dashboard. A free account includes 500 rows, one time, with no credit card.
2. Choose your scope. You can search by city, category (specialty), rating, keyword or status. Start narrow — one specialty in one city — to check the output before you scale.
3. Decide on enrichment. Turn on profile + email enrichment if you need websites and public emails; turn on reviews if you need the second file.
4. Check the pre-flight estimate. Before anything runs, the estimator shows the expected row count, credit cost and runtime. If the number is far larger than you expected, tighten the scope now rather than paying for rows you will discard.
5. Pick a format — CSV, JSON or XLSX — and click Get Data.
6. Download and keep a local copy. Exports auto-delete after 30 days, so store the file wherever your team works with it.

Every run queries Jameda live, so you get what the platform shows today rather than an old snapshot. Large jobs split into batches automatically and come back as one deduplicated file; a single task can run up to 500,000 rows, which is more than enough for a nationwide pull of several specialties.

Scoping tips that save money

• Search by specialty, not by "Arzt". A broad keyword returns every discipline, and most teams only sell to a handful. Run each relevant specialty as its own search.
• Use the rating filter for benchmarking, not for prospecting. Filtering prospects by rating throws away practices that may need your product most.
• Work city by city for territory work. It keeps files small, maps cleanly onto sales territories, and lets you refresh one area without re-pulling the country.

Who uses German doctor data, and how

Medical-device and pharma field sales: territory planning

Field teams live and die by territory design. With latitude, longitude, city and region on every row, you can count, say, orthopaedic and trauma surgery practices per region, cluster them into drivable routes, and balance territories by number of practices rather than by postcode lists inherited from years ago. review_count works as a rough proxy for patient volume, so reps can visit the busiest practices first. Say you pull 2,300 orthopaedic profiles nationally: sorting by region and review count gives each rep a ranked list for their patch in an afternoon, at a cost of a few dollars.

Healthcare SaaS: practice software, booking and telemedicine

This is where the online-booking columns shine. A booking-software vendor can split the list into practices with has_calendar false (no online booking on their profile — the greenfield segment) and practices that already have it (a switching conversation). A telemedicine provider can look at is_online_only to understand how many profiles present themselves that way in each specialty. Practice-management vendors can segment by entity_type and practice_type to separate single-doctor practices from larger group practices, which usually buy differently.

Physician recruitment

Recruiters and hospital HR teams use specialty and location to map where the specialists they need actually practise. A count of dermatologists by city, combined with practice type, shows which areas are saturated and which are thin — useful both for sourcing candidates and for telling a client realistic things about a hard-to-fill role. Recruitment outreach is still outreach, though, so the rules further down apply.

Market research: specialist density by city

Divide the number of profiles in a specialty by a city's population and you have a supply-side density figure you can compare across cities. It is not an official statistic — it counts profiles on one platform — but it is consistent, current and cheap to refresh, which makes it a strong input for site selection for a new practice, a pharmacy or a medical supply branch. Our guide to sizing a local market from business data walks through the same supply-side method using Maps, and it transfers directly.

Reputation benchmarking for clinics and practice groups

A clinic group wants to know how its sites compare with competitors in the same specialty and city. The main file gives rating and review_count for everyone; the reviews file gives the text and dates. Compare average rating, review velocity over the last twelve months (from review_date) and recurring themes in review_text — waiting times, staff friendliness, appointment availability. The same approach we describe for competitor analysis with Google Maps reviews works on Jameda reviews, and running both gives you a fuller picture of how patients talk about a practice.

How do you combine Jameda data with Google Maps?

Jameda tells you what kind of doctor a practice is; Google Maps tells you how the practice presents itself to everyone else. Put them together and the list gets far more useful.

Run the same city through the Google Maps Data Scraper with healthcare categories. The Maps export adds business_phone, working_hours, business_status (so you can drop closed practices), is_verified for the Google listing, and its own average_rating and total_reviews. Then match the two files. The Jameda export includes a google_maps_url column, which can help you link a profile to its Maps listing; where that is not enough, match on a normalised street address plus practice name, and use the coordinates as a tie-breaker.

The result is a row per practice with Jameda's specialty and booking signals alongside a callable phone number, opening hours and a second, independent reputation score. A practice rated well on Jameda but poorly on Google — or the reverse — is itself a useful finding for a reputation-management pitch. For more on the Maps side of healthcare prospecting, see our guide to Google Maps lead generation for healthcare professionals, and for the merge itself, the techniques in cleaning and deduplicating Google Maps data apply equally to combined files.

Is scraping Jameda legal, and can you contact the doctors?

This section is a summary to orient you, not legal advice. Germany is one of the strictest markets in Europe for direct marketing, and healthcare adds its own layer. Take your planned outreach to a lawyer who knows UWG and GDPR before you send anything.

Collection. Livescraper accesses the same public information any logged-out visitor sees on Jameda — nothing behind a login or paywall. Exports auto-delete after 30 days on our side. Our broader guide to the legality of scraping explains the access, contract, data protection and database-right questions in more depth.

GDPR still applies. These are professional profiles, but a doctor's name is personal data, and a single-doctor practice is in effect a person. If you store and use the data you need a lawful basis — commonly legitimate interest, which requires a documented balancing test — and you must tell people where you got their details when you first contact them, honour objections promptly, and keep a suppression list that survives your next refresh. Collect only the columns your purpose needs.

Outreach under UWG §7. The Act against Unfair Competition treats channels differently:

• Phone calls to businesses need at least presumed consent — a concrete reason to believe this practice would want this call, typically because your offer relates directly to its activity. A generic sales call to a list is risky.
• Advertising email is stricter: as a rule it requires the recipient's prior express consent, and this applies to businesses as well as consumers. The main exception covers existing customers under tightly defined conditions. In practice, a cold sales email to a practice scraped from a directory is the high-risk end of German outreach, and enforcement often comes from competitors and associations through warning letters rather than from regulators.
• Postal mail addressed to a practice is generally the most permissive channel, provided you respect objections.

Healthcare-specific rules. Doctors work under professional codes set by the regional medical chambers, and the promotion of medicines and medical devices to healthcare professionals is regulated in its own right. Pharma and device teams should run campaigns past compliance as they normally would — the data source does not change those obligations.

Reviews need extra care. Review text is written by patients and can mention health conditions. Use it in aggregate — themes, ratings, trends — and never try to identify reviewers behind a reviewer_alias.

What does the Jameda scraper cost?

Pricing is pay-as-you-go with no subscription. Every new account gets its first 500 rows free, one time, with no credit card required. After that it is $0.002 per row — roughly $2 per 1,000 records. The pre-flight estimator shows the credit cost of a run before you start it, so check it there rather than estimating by hand. Credits are only spent on rows you actually pull.

To put that in context: a pull of 5,000 general practitioners across your sales region costs $10 at $0.002 a row, or $9 if your 500 free rows are still unused. Refreshing that German doctor database quarterly to catch new practices, closures and profiles that have added online booking costs the same again — less than an hour of a sales rep's time.

A worked example: booking software in three cities

A company selling online appointment software wants to approach dental and orthodontic practices in Munich, Cologne and Hamburg. They run each specialty per city, with website and email enrichment on — about 3,800 profiles in total, illustratively.

First they deduplicate on result_id and group doctors who share a practice_name and address_id, because the buyer is the practice, not each dentist in it. That collapses the list to around 2,600 practices. Next they split on has_calendar: practices without online booking on their profile form the core segment, about 1,400. Within it, practices with a high review_count are prioritised, since a busy practice feels the cost of phone-only booking the most.

They then match the list against a Google Maps pull of the same cities to add business_phone and working_hours, and drop anything Maps marks as closed. Because cold email in Germany generally needs prior consent, the team leads with letters and with calls where they can argue a concrete, specific reason for interest, logs every objection against the practice, and lets the website and email columns serve research and later, consent-based follow-up rather than a cold email blast. A quarterly refresh then shows which practices have since added a booking calendar.

Conclusion

Jameda is the most specialty-aware public source of German doctor data, and scraping it gives you a list you can plan territories, prospect, recruit and benchmark from: 29 core columns covering identity, practice, specialty, reputation, trust badges, online booking and location, plus optional websites, public emails and a separate reviews file. Scope searches by city and specialty, check the pre-flight estimate, combine with Google Maps for phones and hours, and treat German outreach rules as a design constraint rather than an afterthought. Start with the free 500 rows in the Jameda Scraper and see what one specialty in one city looks like before you scale.

Related reading: Google Maps Lead Generation for Healthcare Professionals, Is Scraping Google Maps Legal? A Practical Compliance Guide, How to Size a Local Market Using Google Maps Business Data.