Two datasets on one government domain: a conditions library that lives for months and a service directory that is wrong within weeks.
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CSV · Excel · JSON · XML · FTP · SFTP · Amazon S3 · Google Cloud
API, JSON feed or CSV, XLSX and JSON files
ScheduleOne-time, monthly, weekly, daily or 3 times a day
Healthdirect pages, filters, fields and a schedule
Built, monitored and repaired by our team
API, JSON feed or CSV, XLSX and JSON files
ScrapeIt runs the collection as a managed service. You say whether you need the service directory, the conditions library or both; we build them as separate pipelines because they are different problems, run each on a cadence that matches, and hand back CSV, JSON, Excel or a push into your warehouse with hours structured and changes as their own records.
Service records are refreshed often because their whole value is currency. Conditions content runs monthly or quarterly with the published review date as the trigger.
This is public information published for the public, and we collect it politely: requests paced, nothing behind a login. If your product helps people find care, that carries a duty of accuracy and we will design the cadence around it.
Service records carry the organisation name, service type, full address with postcode, state, coordinates where published, telephone, website, opening hours structured by day including after hours arrangements, the services offered and any access notes.
Opening hours are delivered as structured rows rather than text, which is the single most common reason a client arrives having tried this themselves. Hours as a string cannot drive an application; day, open time, close time with exception flags can.
Geography is delivered ready to join: postcode parsed, state recorded, coordinates where available, so records can be mapped against population or service coverage without another cleaning pass.
Conditions library records carry the page title, condition or medicine name, section structure, summary, symptom and treatment sections as separate fields, related topics, the last reviewed date where published and word count.
Every row carries the collection timestamp, which on the service side is what tells a downstream application whether an opening time can still be trusted.
Coverage gap analysis is a genuine research use. Which regions have thin provision, which service types are missing where, and how availability differs between metropolitan and remote areas are questions asked by researchers, planners and anyone building a health service product, and they need the directory complete rather than sampled.
Related official sources are usually worth joining. Australian health data is spread across several national and state publications, and a directory product typically needs more than one. We scope that at the start rather than delivering a single source and letting the gaps appear later.
Change records on the service side are the operational output: this practice changed its hours, this service stopped appearing, this one began accepting patients. Downstream that becomes an alert rather than a full reload.
Healthdirect is the Australian government health information service. It publishes two things that people routinely conflate, and treating them as one project is how an Australian health brief goes wrong.
The first is a conditions and medicines library: pages about illnesses, symptoms, treatments and when to seek care, written for the public and maintained on review cycles. As data it behaves like reference content - slow, heavily interlinked, valuable for coverage and wording.
The second is a national service directory: general practices, pharmacies, hospitals, emergency departments and specialist services, each with an address, contact details, opening hours and the services offered. As data it behaves like infrastructure - geographic, constantly changing in small ways, and valuable entirely in the fields.
Both surfaces respond directly. The service directory in particular carries the same currency problem as any health locator: a record that is a month old can send somebody to a closed door, and here that somebody may be looking for urgent care.
Delivery: CSV, JSON or XLSX files, our API or a JSON feed. MCP server on request.
Full plan detailsRunning both pipelines on one schedule guarantees one of two failures: either you pay repeatedly to re-read a conditions library that has not changed, or you ship service records that went stale weeks ago.
The conditions library is slow. Pages are reviewed on cycles, the review date is published, and monthly or quarterly collection with the review date as a trigger is sufficient for any content or coverage analysis.
The service directory is not. Practices change hours, close, merge and relocate without announcement anywhere a crawler would notice - the record simply changes. Frequent re-collection with change detection is what keeps it usable, and change detection matters as much as frequency: a record that moved should be flagged rather than silently overwritten, and one that vanished marked rather than dropped.
The stakes are higher than on a commercial locator. A person using an Australian health service directory may be looking for after hours or urgent care, and an address that is wrong is not a data quality complaint. Anyone building a product on this should design the refresh cadence around that rather than around a convenient interval.
The third consideration is geography, which in Australia means distance. Service availability differs enormously between metropolitan, regional and remote areas, and a national summary describes nowhere. The state and postcode fields are what let an analysis segment properly.
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 both 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 regions you actually cover, with hours structured and change records populated from a real observation window.
Still unsure? Write to info@scrapeit.io
Because the halves age at completely different speeds. Conditions content moves on review cycles; service records go stale in weeks. One schedule means either paying to re-read static content or shipping opening hours that are wrong, and both are avoidable by scoping them separately.
As current as the refresh you buy, and this is the source where it matters most. Practices change hours, close, merge and relocate with no announcement anywhere a crawler would see. We re-collect frequently and deliver changes as records, so a moved service is flagged rather than silently overwritten and a vanished one marked rather than dropped.
Yes, structured by day with open and close times plus flags for exceptions and after hours arrangements. Hours as a text string cannot drive an application, and this is the most common reason clients arrive having tried the collection themselves.
Yes. Service listings with addresses, telephone numbers and opening hours, and the conditions and medicines library, are published for the public. We collect only publicly available data - everything a visitor can see on Healthdirect - and we collect it legally.
No. The directory is organisational information about services, and the 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 and pace requests.
You share your needs, expectations, and desired timeframe. We'll suggest the best solution based on your request and budget.
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
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.
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.
Tell us which sites, which fields and how often. A couple of lines is enough to start.
Healthdirect data from €169/month. Free project assessment, reply within 1 business day.