Two entirely different datasets live on one domain: a conditions library that is content, and a service directory that is infrastructure. They need different pipelines.
ScrapeIt runs the collection as a managed service. You say whether you need the service directory, the conditions library or both; we build the pipelines separately because they are different problems, run them on cadences that match, and hand back CSV, JSON, Excel or a push into your warehouse, with hours structured, postcodes standardised and changes delivered as their own records.
Cadence is set by the data rather than by preference. Service records are refreshed often, because their whole value is currency. Conditions content runs monthly or quarterly with the published review date as a trigger.
This is public information published for the public, and we collect it politely: crawl rules honoured, requests paced, nothing behind a login. Licence terms are checked per item before reuse rather than assumed, and we tell you what they permit. If your product will help people find care, that carries a duty of accuracy, and we will design the refresh cadence around it. Bring the use case to your own counsel before the project starts.
Service records deliver the organisation name, service type, full address with postcode, latitude and longitude where published, telephone, website, opening hours by day including variations for holidays where shown, the services offered, accessibility information where published, and whether the practice is accepting new patients where that is stated.
Opening hours are the field that has to be structured rather than stored as text. Delivered as a string, they are useless to any application; delivered as day, open time, close time, with a separate flag for closures and exceptions, they drive a store locator directly. This is the single most common reason a client comes to us having tried it themselves.
Geography is delivered ready to use: postcode parsed and standardised, coordinates where available, and the administrative area, so records can be joined to population data or to a service coverage map without another cleaning pass.
Conditions library records deliver page title, condition or medicine name, section structure, summary, symptom and treatment sections as separate fields, related conditions and internal links, the last reviewed and next review dates where published, and word count.
Every row carries the collection timestamp, which on the service side is not decoration: it is what tells you whether an opening time can still be trusted.
Licensing deserves a careful sentence rather than a confident one. Public sector information in the United Kingdom is often published under open government licensing, and material on this domain frequently falls under such terms, but the licence applies per item and there are exclusions, notably logos and third party content. We check the licence that actually applies to the material a client wants and tell them what it permits, rather than asserting that everything on a government domain is free to reuse. That distinction has caught out plenty of products.
Change detection is the mechanism that makes the service side worth buying. Every collection is compared with the last, and differences are delivered as their own records: this practice changed its hours, this pharmacy stopped appearing, this dentist began accepting patients. Downstream that becomes an alert rather than a full reload.
Related official sources are usually worth joining. Health service data in the United Kingdom is spread across several official publications and registers, and a directory product typically needs more than one. We scope that at the start rather than delivering one source and letting the gaps appear later.
Historical work on the conditions library is practical because URLs are stable and review dates are published. Tracking when guidance was last revised, and which pages have gone untouched for years, is a straightforward periodic comparison.
The NHS website carries two things that people routinely conflate, and treating them as one project is the usual way an NHS brief goes wrong.
The first is the conditions library: an alphabetical set of pages covering illnesses, symptoms, treatments and medicines, written for the public and maintained on review cycles. As data it behaves like reference content. It changes slowly, it is heavily internally linked, and its value is in coverage and wording rather than in any field.
The second is the service directory: general practices, pharmacies, dentists, opticians, hospitals and urgent care, each with an address, a postcode, contact details, opening hours and the services it offers. As data it behaves like infrastructure. It changes constantly in small ways, it is geographic, and its value is entirely in the fields.
They need different pipelines, different cadences and different quality checks. Reference content is worth re-collecting monthly with the review date as a trigger. A pharmacy opening hours dataset that is a month stale is actively misleading, because the whole point of it is to tell somebody where they can go right now.
Both surfaces are public and both are reachable: the conditions index and the service search both respond, and service pages are addressable per location and per service type.
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A conditions page that is six weeks old is fine. A pharmacy record that is six weeks old may send somebody to a closed door, and if it sits inside a product that promises to help people find care, that is not a data quality issue, it is a safety issue with a support ticket attached.
Practices change opening hours, stop accepting new patients, merge, move and close. None of that is announced anywhere a crawler would notice; the record simply changes. So the useful design is frequent re-collection of the service side with change detection, so that a record which moved is flagged rather than silently overwritten, and a record that vanished is marked rather than dropped.
The second reason is geography. Service data is only useful at a location, and the questions people ask of it are all spatial: what is open near this postcode, how many pharmacies serve this area, where is the nearest dentist accepting patients. That means postcodes and coordinates have to be right, standardised and joinable on arrival, not fixed later.
The third is that coverage gaps are themselves a finding. Which areas have thin provision, which service types are missing where, how availability differs between regions. Those questions get asked by researchers, by commissioners and by anyone building a health service product, and they need the directory complete rather than sampled.
The conditions library, by contrast, is a slow content dataset and should be scheduled and priced like one.
Learn how to use web scraping to solve data problems for your organization
Leveraging advances in technology, the AI-powered web scraper has skyrocketed in demand and is helping to expand capabilities by automating tedious daily tasks and speeding up data collection from thousands of websites several times over.
Web scraping is a method of obtaining web data by extracting it from pages of web resources with the help of a program, that is, in automatic mode. It is used to syntactically convert web pages into more usable forms.
If you specialize in machine learning, you need to feed large amounts of data to the algorithms. Web scraping is the easiest and the most efficient method of collecting the data from all over the Internet.
ScrapeIt is a managed extraction company, not a tool you have to learn. Our team builds the pipelines, watches them as the site changes, and repairs them before your directory starts sending people to the wrong address.
You see a sample first, in your format, over the service types and areas you actually cover, with hours structured and change records populated from a real observation window.
Sometimes, and the answer is per item rather than blanket. Public sector information here is often published under open government licensing, but licences apply to specific material and carry exclusions such as logos and third party content. We check what actually applies to the material you want and tell you what it permits, rather than assuming that anything on a government domain is free to reuse.
As current as the refresh you buy, and this is the source where that matters most. Practices change hours, stop accepting patients, merge and close without announcement. We re-collect frequently and deliver changes as their own records, so a moved practice is flagged rather than silently overwritten and a vanished one is marked rather than dropped.
Yes, structured by day with open and close times, plus flags for closures and exceptions where they are shown. Opening hours as a text string are useless to any application, and this is the most common reason clients arrive having tried the collection themselves.
Yes, but as two pipelines rather than one. They are different problems: reference content that changes on review cycles and infrastructure data that changes constantly. Running them on one cadence means either paying to re-read static content or shipping stale opening hours, and both are avoidable.
No. The service directory is organisational information about practices and pharmacies, and the conditions library is published guidance for the public. There is no patient information on these surfaces and we collect none. We work only on public pages, never behind a login, and we pace requests.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
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