Finding private nursing homes in Northern Germany with open staffing needs

Germany has around 16,500 nursing homes licensed under SGB XI (the long-term care insurance code). About 42% are privately operated, around 53% are non-profit (Caritas, Diakonie, AWO, German Red Cross, etc.), and the rest are public (KBV, based on the 2023 Destatis care statistics; the federal government cites roughly 43% private residential facilities). For Northern Germany, that means several thousand facilities, of which you only need a fraction.

Why is building the list by hand so tedious?

• Ownership type is not cleanly listed anywhere. The insurers' search portals (AOK-Pflegenavigator, vdek-Pflegelotse) list facilities, but not reliably whether a private operator's GmbH, a non-profit association or a non-profit gGmbH is behind them. You only find out from the legal notice (Impressum): legal form, shareholders, "non-profit" status, association membership.
• Demand is fleeting. Whether a home is hiring nurses shows up on its careers page, on the Federal Employment Agency's job board or on Indeed: yes today, gone in six weeks. No list broker keeps this current day by day.
• Facility directors change frequently and are often listed only on the individual home's page ("Your contacts"), not in the group's legal notice. For chains, the legal notice names only the managing directors at headquarters.
• Chains obscure their locations. Large private operators have a separate subpage per home, sometimes with its own email, sometimes with only a central application portal.

Realistically, a researcher needs 8 to 15 minutes per facility (map, website, legal notice, careers page, team page). For 2,000 homes that is 270 to 500 hours of work, and much of it goes to homes that end up not qualifying (non-profit, no hiring need).

Haystack collects every nursing home in the region with a Google Maps grid search, then runs AI columns in stages: cheap filtering first (Is this actually a residential nursing home? Is it private?), expensive research only after that (job postings, facility director, email). You only pay for research time on the homes that deserve it.

The result is a table you receive via a password-protected share link or as an Excel file: only qualified homes, each with reasoning, source and score. Verifying the hits is still up to you, but instead of hundreds of hours of raw research you spot-check a finished list.

The same staged approach works in very different industries, for example Toronto fashion D2C brands on Shopify that offer free returns or Fort Worth warehouses hiring forklift operators.

1. Create the project

Create a project called "Private nursing homes North – registered nurses". The standard tools (including web search) are attached automatically. Add AiBrowser_Tool: the AI browser visits the legal notice, careers page and "Team" or "Contacts" pages.

2. Search terms and search area

Create the search as a draft. Search terms (several per search; the German queries Google Maps actually needs):

• Pflegeheim (nursing home)
• Altenheim (retirement home)
• Seniorenheim (senior home)
• Seniorenresidenz Pflege (senior residence with care)
• vollstationäre Pflege (full residential care)

For the search area, draw a grid over Hamburg, Schleswig-Holstein, Bremen, Lower Saxony and Mecklenburg-Western Pomerania, or assign a saved grid favorite "Northern Germany". In dense cities (Hamburg, Bremen, Hanover, Kiel, Lübeck) the grid should be finer, because Google Maps returns only a limited number of results per search point.

After the run you have raw data per business: name, address, phone, website, category, rating and number of reviews. Expect false positives too: home care services, adult day care, assisted living. That is exactly what stage 1 is for.

Save proven columns such as traeger or einrichtungsleitung as a global template, and they are ready to go on your next project (e.g. home care services in North Rhine-Westphalia). For another example of reaching the actual decision-maker instead of headquarters, see IT staffing agencies in Central New Jersey placing Java and SAP talent on C2C.

5. Share link and Excel export

Set up the share view for the staffing agency (or its sales team):

• Filter: offene_stellen_pfk contains "JA" and einrichtungsleitung not empty.
• Visible columns: name, city, phone, website, operator, job title + source, facility director, email, score.
• Pinned column: name. Sort: score descending.
• Merge locations so homes with several Maps listings do not appear twice.
• Optional "All companies analysed" tab with status and reasoning, useful to see why a well-known home is missing (e.g. "GEMEINNUETZIG").

The link is password-protected. The Excel export uses the same filter.

• Google Maps is not complete. Homes with no category or the wrong one are missing. If you need completeness, cross-check against the insurers' care portals.
• Ownership is not always clear-cut. Private operators with a non-profit subsidiary, or lease models, create edge cases, which is why the operator name is in the column.
• Hiring demand is a snapshot. A list is out of date after a few weeks; for ongoing prospecting, rerun the pipeline regularly.
• Facility director names are not published everywhere. Expect a meaningful share of NOT_FOUND, which beats guessed names.
• Haystack does not send emails or sync with a CRM. You handle the outreach yourself.

The name and work email of a facility director are personal data. Processing them for B2B prospecting can be based on legitimate interest (Art. 6(1)(f) GDPR); this includes the duty to inform under Art. 14 GDPR at first contact and a way to object.

A separate question is whether you may email the address: under Section 7(2) no. 2 of the German Act Against Unfair Competition (UWG), advertising by email without prior express consent is generally an unreasonable nuisance, even toward businesses. Calling businesses is permitted with presumed consent, which can be well justified for a home that is publicly advertising nursing positions. If in doubt, have your approach reviewed by a lawyer; this article is not legal advice. The list itself is just as useful for phone, mail and in-person outreach.

• KBV, Facts & figures: nursing homes and staff (based on Destatis care statistics 2023, German)
• German Bundestag, hib 248/2024 (share of private operators of residential facilities, German)
• German Act Against Unfair Competition (UWG), Section 7 (German)
• GDPR Art. 6 (Lawfulness of processing)
• GDPR Art. 14 (Information where data not obtained from the data subject)