WebMD is a publisher, not a clinical authority, and the difference matters for what the data is fit for. As a map of consumer health search it has few rivals.
ScrapeIt runs the collector as a managed service. You name the drug list, condition set or topic centres; we build the pipeline, run it on your cadence and hand back CSV, JSON, Excel or a push into your warehouse, with fields split out, interactions as pairs and advertising modules stripped from the body.
Cadence here is slower than on a news source and that is deliberate. Reference content changes on review cycles rather than hourly, so most clients run monthly or quarterly refreshes with the reviewed date as the trigger for a closer look.
We collect what the site renders publicly, we pace requests, and we collect nothing that identifies a reviewer. WebMD content is copyrighted and their terms restrict automated collection and reuse, so the dataset is built for analysis rather than republication. This is consumer publishing and not clinical guidance; if your use case touches patient care, that needs authoritative sources and your own clinical governance. Bring it to your counsel before the project starts.
Drug pages deliver drug name, generic and brand names, drug class, dosage forms and strengths, uses, how to use, side effects, precautions, interactions, overdose guidance and storage, each as its own field rather than one wall of text.
Interaction data is delivered as rows: this drug, that drug, and the severity level as published. Kept flat as prose it is unusable; kept as a pair list it is a graph you can query, which is the shape almost every client actually wants.
User ratings and reviews are collected as aggregate figures and, where a client needs them, as individual entries with the rating, condition treated, date and review text. We do not collect anything that identifies the reviewer as a person, and we say so up front, because a health review tied to an identity is sensitive data by any reading.
Condition and article pages deliver headline, standfirst, topic centre, medical reviewer and reviewed date where published, body text with advertising and sponsored modules removed, internal links, and word count.
The medical reviewer field is worth its own mention. Consumer health content on this site is typically marked with a reviewer and a review date, and those two fields are the closest thing to a freshness signal the source offers.
Separating editorial from commercial modules is not optional here. Sponsored blocks, partner units and ad slots live inside the article templates, and leaving them in the body means your term frequencies, your word counts and any model you train inherit advertising copy. We identify and strip them, keeping a flag that records the page carried sponsored content, because that flag is itself informative.
Review collection is scoped narrowly and deliberately. Aggregate rating and count are safe and useful. Individual review text is available where a client needs it, without any reviewer identity, and we would rather discuss why the identity is not needed than hand over a file that turns a health complaint into personal data.
Cross source verification is worth building in from the start. Consumer publishing summarises; regulatory labels are authoritative. Where a client is doing anything that has to be accurate about a medicine, we join to authoritative sources rather than treating a consumer monograph as the record. The consumer page tells you what the public is being told; the label tells you what is true.
Historical work is practical because article URLs are stable. Tracking how a condition centre grew, or when a monograph was last reviewed, works as a periodic re-collection rather than an archive pull, since the site does not expose its own version history.
WebMD is the largest consumer health publisher in the United States and the site most ordinary people land on when they search a symptom or a medicine. That is what makes it worth collecting: it is a map of what the general public reads about health, not a clinical reference.
The structure splits into two large libraries. The drug section carries monograph pages per medicine, with uses, dosing, side effects, precautions, interactions and a user rating and review section. The conditions and health topic section carries editorial articles, symptom guides, slideshows and quizzes, organised into topic centres.
A drug index page lists the database alphabetically and is the practical entry point for enumerating the drug library. Condition content is reachable through topic centres and internal navigation rather than a single index, so collection there leans on section crawling.
Commercially the site is heavily monetised, which shows up in the data: sponsored modules, partner content and advertising units sit inside the page templates. Any collector that does not separate editorial body text from those units will end up analysing advertising copy, and the results look very odd once somebody notices.
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Being clear about fitness for purpose is the whole first paragraph of any WebMD project. This is consumer publishing. It is well reviewed consumer publishing, but it is not a regulatory drug label, not a clinical guideline and not a substitute for either. A dataset built from it should feed content strategy, coverage analysis and search research, and it should not feed anything that makes a clinical decision.
Within that scope it is unusually good. Because it ranks for an enormous share of consumer health queries, mapping its coverage tells you exactly which conditions, medicines and symptoms are being served with content and how deeply. For a health publisher, a pharmacy chain or a telehealth service planning where to compete, that map is the whole starting point.
The second use is completeness auditing against your own catalogue. If you publish drug or condition information, the useful question is which entries exist here and not in your library, and where your coverage is thinner. That is a straightforward set difference once both sides are structured.
The third is the review corpus. Patient reported experience with a medicine, at scale and per condition, is a genuine signal for market research, though it is self selected and must be treated as such. We deliver it with that caveat attached rather than as if it were evidence.
The fourth is freshness. Reviewer names and review dates make it possible to see which parts of the library are being maintained and which have been left alone for years, and that is often a competitor's soft spot.
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 pipeline, watches it as templates and ad units change, and repairs it before your analysis goes stale.
You see a sample first, in your format, over the drugs or conditions you actually care about, with the commercial modules already separated so you can judge the text fields on real pages.
No, and we would rather say it in the first answer than the last. This is consumer health publishing: useful, reviewed, and not a regulatory label or a clinical guideline. It is fit for content strategy, coverage analysis and search research. Anything that touches patient care needs authoritative sources and your own clinical governance, and we will help you join to those instead.
Yes, as rows rather than prose: this drug, that drug, severity as published. That shape makes it a graph you can query instead of a paragraph you have to read. As with everything from this source, it reflects what the publisher states and is not a substitute for an authoritative interaction database.
We collect aggregate ratings and counts by default, and individual review text where a client needs it, with the rating, the condition treated and the date. We never collect anything that identifies the reviewer. A health complaint attached to a person is sensitive data by any reading, and there is no analysis that needs it.
By identifying the sponsored and partner modules in the templates and stripping them, while keeping a flag that the page carried sponsored content. Left in, they contaminate word counts, term frequencies and any model trained on the text, and the contamination is invisible until somebody reads a sample closely.
Slower than you would think. Reference content changes on editorial review cycles rather than continuously, so monthly or quarterly suits most briefs, using the published reviewed date as the signal for a closer look. Delivery is CSV, Excel, JSON, JSONLines or XML over FTP, SFTP, Amazon S3, Google Cloud Storage, Dropbox, Google Drive or email, or written straight into your database.
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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