# Healthline Articles Scraper (`automation-lab/healthline-health-article-dataset`) Actor

Extract supplied public Healthline articles into structured text, headings, authors, medical-review attribution, dates, topics, citations, media, canonical URLs, and change-detection hashes.

- **URL**: https://apify.com/automation-lab/healthline-health-article-dataset.md
- **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community)
- **Categories:** Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.44 / 1,000 item extracteds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Healthline Articles Scraper

Turn supplied public **Healthline articles** into structured records for content research, medical-review auditing, citation analysis, and recurring update monitoring.

Provide one or more Healthline article URLs. The Actor returns one dataset item per successfully extracted article with its title, clean body text, ordered headings, author and medical reviewer, publication and update dates, topic metadata, citations, media, canonical URL, and a stable content hash.

### What does Healthline Articles Scraper do?

The Actor reads public, server-rendered Healthline article pages.
It combines Healthline's structured article metadata with fields extracted from the visible article body.

Use it to:

- build a structured Healthline article dataset;
- compare `dateModified`, `lastReviewed`, and `contentHash` across scheduled runs;
- audit author and medical-review attribution;
- collect cited external sources for research;
- preserve headings and normalized article text for downstream analysis;
- export article records to JSON, CSV, Excel, databases, or automation tools.

This Actor processes URLs you supply.
It does not search Healthline, discover every article, or send change alerts by itself.
Schedule it in Apify and compare datasets downstream when you need monitoring.

### Who is this Healthline article extractor for?

**Health-content researchers** can collect consistent article text and citation lists.

**Editorial and compliance teams** can track medical reviewer attribution and review dates.

**SEO and content strategists** can compare headings, topics, word counts, and source links.

**Data engineers** can feed typed records into warehouses, spreadsheets, search indexes, or NLP pipelines.

**Monitoring teams** can rerun a saved Task and compare `contentHash` or `dateModified` to detect likely changes.

### Why use this Actor?

- **Source-specific extraction:** fields are mapped to Healthline's article and medical-review metadata.
- **Monitoring-ready identity:** every record includes requested and canonical URLs.
- **Deterministic comparison:** SHA-256 `contentHash` is derived from normalized article text.
- **Useful attribution:** authors and medical reviewers are separate fields.
- **Research context:** external cited links and available media are returned with the article.
- **Low-overhead route:** direct HTTP and server-rendered structured data avoid browser cost.
- **Failure visibility:** invalid URLs fail closed, and a run with no useful articles fails rather than returning silent success.

### What data can you extract?

| Field | Meaning |
| --- | --- |
| `url` | URL supplied in the run input |
| `canonicalUrl` | Canonical Healthline URL |
| `title` | Article headline |
| `description` | Source-provided article summary, when available |
| `authors` | Named article authors |
| `medicallyReviewedBy` | Medical reviewer attribution, when shown |
| `datePublished` | Source publication timestamp, when available |
| `dateModified` | Source modification timestamp, when available |
| `lastReviewed` | Source medical-review timestamp, when available |
| `articleSections` | Structured section or category labels |
| `keywords` | Source-declared topic keywords |
| `headings` | Ordered H2–H4 headings with levels |
| `articleText` | Normalized visible article-body text |
| `wordCount` | Word count for `articleText` |
| `citations` | External links cited inside the article body |
| `media` | Image and video URLs with available captions |
| `contentHash` | SHA-256 hash of normalized article text |
| `scrapedAt` | UTC timestamp for this extraction |

Nullable fields remain `null` when Healthline does not provide the corresponding metadata.
Arrays remain empty when no matching values are present.

### How to scrape Healthline articles

1. Open the Actor in Apify Console.
2. Add one or more public `https://www.healthline.com/...` article URLs.
3. Set **Maximum articles** to bound the run.
4. Click **Start**.
5. Open the default dataset to inspect the article records.
6. Export the dataset or connect it to your next workflow.

A useful first run is:

```json
{
  "startUrls": [
    { "url": "https://www.healthline.com/nutrition/10-health-benefits-of-apples" },
    { "url": "https://www.healthline.com/health/type-2-diabetes" }
  ],
  "maxItems": 2
}
```

### Input parameters

#### `startUrls`

Required array of public HTTPS Healthline article URLs.
String URLs and Request List source objects are accepted by the Actor runtime.
Duplicate normalized URLs are processed once.
Any malformed, non-HTTPS, or non-Healthline URL is rejected before scraping starts.

#### `maxItems`

Maximum number of unique supplied URLs to process.

- default: `20`
- minimum: `1`
- maximum: `1000`

The limit is applied before requests are made.
Supplying 100 URLs with `maxItems: 10` processes at most the first 10 unique URLs.

### Output example

A real article produces a record shaped like this:

```json
{
  "url": "https://www.healthline.com/nutrition/10-health-benefits-of-apples",
  "canonicalUrl": "https://www.healthline.com/nutrition/10-health-benefits-of-apples",
  "title": "10 Impressive Health Benefits of Apples",
  "description": "Apples are a popular fruit grown and enjoyed around the world...",
  "authors": ["Ariane Lang, BSc, MBA"],
  "medicallyReviewedBy": "Jerlyn Jones, MS MPA RDN LD CLT",
  "datePublished": "2018-12-17T08:00:00Z",
  "dateModified": "2026-07-20T11:57:40Z",
  "lastReviewed": "2026-07-20T04:56:49Z",
  "articleSections": ["Uncategorized", "Cat 1"],
  "keywords": ["benefits of apples"],
  "headings": [
    { "level": 2, "text": "Key Takeaways" },
    { "level": 2, "text": "1. Nutritious" }
  ],
  "articleText": "Key Takeaways Apples are rich in fiber...",
  "wordCount": 1042,
  "citations": [
    { "title": "research", "url": "https://pubmed.ncbi.nlm.nih.gov/..." }
  ],
  "media": [
    { "type": "image", "url": "https://media.post.rvohealth.io/...jpg", "caption": null }
  ],
  "contentHash": "c89c4ee9a413...",
  "scrapedAt": "2026-09-06T14:20:00.000Z"
}
```

The abbreviated text, links, hash, and timestamp above keep the example readable.
Dataset records contain the complete extracted values.

### How much does it cost to extract Healthline articles?

The Actor uses pay-per-event pricing:

- a small one-time **start** event for each run;
- one **item** event for each article record saved to the dataset.

Failed, rejected, or duplicate URLs do not create item charges.
Prices decrease by Apify subscription tier.
The exact active rates appear in the Actor's pricing tab before you start a run.

At the BRONZE rate of $0.002392 per article plus the $0.005 start event:

- 1 article costs $0.007392;
- 10 articles cost $0.02892;
- 100 articles cost $0.24420.

These examples describe Actor event charges.
Your Apify plan and platform usage can affect the final total shown by Apify.
Check the live pricing panel for authoritative current rates.

### Monitor article updates

Save an Apify Task containing stable article URLs and schedule it daily, weekly, or monthly.
For each canonical URL, compare the newest record with the prior run.

Useful change signals include:

1. `contentHash` changed — normalized article text changed;
2. `dateModified` changed — Healthline updated its modification timestamp;
3. `lastReviewed` changed — medical-review metadata changed;
4. `medicallyReviewedBy` changed — reviewer attribution changed;
5. `citations` changed — cited external links changed;
6. `headings` changed — article structure changed.

A hash difference indicates text changed, not what changed.
Store both records or run a diff downstream to identify the exact edit.

### Export and integration workflows

From the dataset page, export records as JSON, JSONL, CSV, XML, RSS, or Excel.
You can also send results to:

- Google Sheets for editorial review;
- Airtable or Notion for content inventories;
- BigQuery, Snowflake, or PostgreSQL for longitudinal analysis;
- webhooks and Make for update automation;
- an NLP pipeline for classification or summarization;
- a search index for internal article research.

Use `canonicalUrl` as the stable join key.
Keep `scrapedAt` to order snapshots.
Compare `contentHash` before running expensive downstream analysis.

### Run with the Apify API

Replace `YOUR_TOKEN` with an Apify API token.

#### cURL

```bash
curl -X POST \
  "https://api.apify.com/v2/acts/automation-lab~healthline-health-article-dataset/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "startUrls": [{"url":"https://www.healthline.com/health/type-2-diabetes"}],
    "maxItems": 1
  }'
```

#### JavaScript

```javascript
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('automation-lab/healthline-health-article-dataset').call({
  startUrls: [{ url: 'https://www.healthline.com/health/type-2-diabetes' }],
  maxItems: 1,
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items[0].title, items[0].medicallyReviewedBy);
```

#### Python

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_TOKEN")
run = client.actor("automation-lab/healthline-health-article-dataset").call(run_input={
    "startUrls": [{"url": "https://www.healthline.com/health/type-2-diabetes"}],
    "maxItems": 1,
})
items = client.dataset(run["defaultDatasetId"]).list_items().items
print(items[0]["title"], items[0]["medicallyReviewedBy"])
```

For synchronous API calls, use Apify's run-and-get-dataset-items endpoint when its timeout is suitable for your batch.

### Use with MCP and AI agents

Add the Actor to Claude Code through Apify MCP:

```bash
claude mcp add --transport http apify \
  "https://mcp.apify.com?tools=automation-lab/healthline-health-article-dataset"
```

Claude Desktop, Cursor, and VS Code can use this MCP server configuration:

```json
{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com?tools=automation-lab/healthline-health-article-dataset"
    }
  }
}
```

Example prompts:

- “Extract the reviewer, review date, headings, and citations from this Healthline article URL.”
- “Run my saved list of Healthline URLs and return the articles whose content hashes differ from my previous dataset.”
- “Collect article text and external sources from these three public Healthline nutrition pages.”

Your agent still needs valid Apify authentication and the exact public URLs.

### Reliability and failure behavior

The Actor uses direct HTTP because Healthline currently exposes article data in server-rendered HTML and JSON-LD.
It does not download image or video binaries.

Transient timeouts, HTTP 429 responses, and server errors are retried with bounded backoff.
Deterministic bad requests are not retried blindly.

When one URL fails and others succeed, the successful records remain in the dataset and the failure appears in the log.
When every supplied URL fails, the run fails non-zero instead of reporting an empty successful result.

Healthline can change its markup or access controls.
Review logs and rerun after transient source incidents.

### Limitations

- Only public HTTPS hostnames on `healthline.com` are accepted.
- The Actor processes supplied article URLs; it does not support keyword search or sitemap crawling.
- It extracts current page state, not historical versions.
- It does not create alerts or calculate a semantic diff.
- Citation extraction returns external links in the article body; it does not verify each linked study.
- `wordCount` is a whitespace-based operational count, not a publishing-standard editorial count.
- Media fields are URLs and captions, not downloaded files.
- Some Healthline pages may omit authors, reviewers, dates, topics, citations, or media.
- Source markup changes can temporarily reduce extraction until the Actor is updated.

### Responsible use and legality

Healthline content is publicly accessible but remains subject to copyright, database, and website terms.
Use the Actor only where you have a lawful purpose and appropriate rights.

Do not republish full copyrighted articles without permission.
Do not present extracted health information as medical advice.
Do not infer credentials or review status beyond what the source explicitly provides.
Avoid collecting or combining data in ways that create sensitive personal profiles.

You are responsible for complying with applicable law, source terms, and your organization's policies.

### Troubleshooting

**The Actor says only Healthline URLs are supported.**
Check that every input uses HTTPS and a `healthline.com` hostname.
Redirector, shortener, and third-party URLs are intentionally rejected.

**A URL returns no article.**
Open it publicly and verify it is an article rather than a topic, search, provider, account, or error page.
Inspect the run log for its HTTP status or parser message.

**One record has no reviewer.**
Reviewer attribution is nullable.
The page may not display medical-review metadata in its structured article record.

**My scheduled run changed hashes.**
Compare `articleText`, headings, citations, and dates against the previous dataset.
A hash is a change signal, not a human-readable diff.

**The run partially succeeded.**
Review failed URLs in the log, correct invalid pages, and rerun only those URLs.
Already saved useful records remain available.

### FAQ

#### Does it search Healthline by keyword?

No.
Supply exact public article URLs.
This narrow contract avoids mixing search-result records with full article records.

#### Can it monitor articles automatically?

Use an Apify Task and schedule for recurring runs.
The Actor supplies `contentHash`, dates, and stable URLs; your downstream workflow compares snapshots or sends alerts.

#### Does it download images or video?

No.
It returns source media URLs and available captions, which keeps runs lightweight.

#### Are citations guaranteed to be medical studies?

No.
They are external links found inside the article body.
Validate source type and quality for your research use case.

#### Why can dates be null?

The fields reflect structured metadata exposed on each page.
The Actor does not invent missing publication, modification, or review dates.

#### Can I send thousands of URLs?

The input limit is 1,000 per run.
Start with a small batch, then schedule bounded Tasks and respect the source.

### Related Automation Lab Actors

- [Multi-Site Article Content Extractor](https://apify.com/automation-lab/multi-site-article-content-extractor) extracts normalized text and metadata from mixed-domain URL lists when you do not need Healthline-specific reviewer and topic fields.
- [PubMed Article Search Scraper](https://apify.com/automation-lab/pubmed-article-search-scraper) searches biomedical literature metadata and abstracts when your workflow starts from a research query rather than a Healthline URL.
- [Multi-page On-page SEO Audit Crawler](https://apify.com/automation-lab/multi-page-on-page-seo-audit) audits technical and content signals across a bounded website crawl.

Choose this Actor when the source is Healthline and medical-review attribution, dates, citations, and change hashes are central to the job.

# Actor input Schema

## `startUrls` (type: `array`):

Public HTTPS article URLs on healthline.com. Duplicate URLs are processed once; non-Healthline URLs are rejected.

## `maxItems` (type: `integer`):

Maximum number of unique supplied URLs to process. Use a small value while testing recurring workflows.

## Actor input object example

```json
{
  "startUrls": [
    {
      "url": "https://www.healthline.com/nutrition/10-health-benefits-of-apples"
    },
    {
      "url": "https://www.healthline.com/health/type-2-diabetes"
    }
  ],
  "maxItems": 20
}
```

# Actor output Schema

## `overview` (type: `string`):

Open structured article records in the overview dataset view.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "startUrls": [
        {
            "url": "https://www.healthline.com/nutrition/10-health-benefits-of-apples"
        },
        {
            "url": "https://www.healthline.com/health/type-2-diabetes"
        }
    ],
    "maxItems": 20
};

// Run the Actor and wait for it to finish
const run = await client.actor("automation-lab/healthline-health-article-dataset").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "startUrls": [
        { "url": "https://www.healthline.com/nutrition/10-health-benefits-of-apples" },
        { "url": "https://www.healthline.com/health/type-2-diabetes" },
    ],
    "maxItems": 20,
}

# Run the Actor and wait for it to finish
run = client.actor("automation-lab/healthline-health-article-dataset").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "startUrls": [
    {
      "url": "https://www.healthline.com/nutrition/10-health-benefits-of-apples"
    },
    {
      "url": "https://www.healthline.com/health/type-2-diabetes"
    }
  ],
  "maxItems": 20
}' |
apify call automation-lab/healthline-health-article-dataset --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,automation-lab/healthline-health-article-dataset"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/juBYmcicPVQcGrzLi/builds/tH5x1tuVCEIKla6HV/openapi.json