Why Healthcare Companies should look towards Web Scraping

Why Healthcare Companies should look towards Web Scraping

The internet is a massive storehouse of information which is available in the form of text, media and other formats. To be competitive in this modern world, most businesses need access to this storehouse of information. But, all this information is not freely accessible as several websites do not allow you to save the data. This is where the process of Web Scraping comes in handy.

Web scraping is not new—it has been widely used by financial organizations, for detecting fraud; by marketers, for marketing and cross-selling; and by manufacturers for maintenance scheduling and quality control. Web scraping has endless uses for business and personal users. Every business or individual can have his or her own particular need for collecting data. You might want to access data belonging to a particular category from several websites. The different websites belonging to the particular category display information in non-uniform formats. Even if you are surfing a single website, you may not be able to access all the data at one place.

The data may be distributed across multiple pages under various heads. In a market that is vast and evolving rapidly, strategic decision-making demands accurate and thorough data to be analyzed, and on a periodic basis. The process of web scraping can help you mine data from several websites and store it in a single place so that it becomes convenient for you to a alyze the data and deliver results.

In the context of healthcare, web scraping is gaining foothold gradually but qualitatively. Several factors have led to the use of web scraping in healthcare. The voluminous amount of data produced by healthcare industry is too complex to be analyzed by traditional techniques. Web scraping along with data extraction can improve decision-making by determining trends and patterns in huge amounts of intricate data. Such intensive analyses are becoming progressively vital owing to financial pressures that have increased the need for healthcare organizations to arrive at conclusions based on the analysis of financial and clinical data. Furthermore, increasing cases of medical insurance fraud and abuse are encouraging healthcare insurers to resort to web scraping and data extraction techniques.

Healthcare is no longer a sector relying solely on person to person interaction. Healthcare has gone digital in its own way and different stakeholders of this industry such as doctors, nurses, patients and pharmacists are upping their ante technologically to remain in sync with the changing times. In the existing setup, where all choices are data-centric, web scraping in healthcare can impact lives, educate people, and create awareness. As people no more depend only on doctors and pharmacists, web scraping in healthcare can improve lives by offering rational solutions.

To be successful in the healthcare sector, it is important to come up with ways to gather and present information in innovative and informative ways to patients and customers. Web scraping offers a plethora of solutions for the healthcare industry. With web scraping and data extraction solutions, healthcare companies can monitor and gather information as well as track how their healthcare product is being received, used and implemented in different locales. It offers a safer and comprehensive access to data allowing healthcare experts to take the right decisions which ultimately lead to better clinical experience for the patients.

Web scraping not only gives healthcare professionals access to enterprise-wide information but also simplifies the process of data conversion for predictive analysis and reports. Analyzing user reviews in terms of precautions and symptoms for diseases that are incurable till date and are still undergoing medical research for effective treatments, can mitigate the fear in people. Data analysis can be based on data available with patients and is one way of creating awareness among people.

Hence, web scraping can increase the significance of data collection and help doctors make sense of the raw data. With web scraping and data extraction techniques, healthcare insurers can reduce the attempts of frauds, healthcare organizations can focus on better customer relationship management decisions, doctors can identify effective cure and best practices, and patients can get more affordable and better healthcare services.

Web scraping applications in healthcare can have remarkable utility and potential. However, the triumph of web scraping and data extraction techniques in healthcare sector depends on the accessibility to clean healthcare data. For this, it is imperative that the healthcare industry think about how data can be better recorded, stored, primed, and scraped. For instance, healthcare sector can consider standardizing clinical vocabulary and allow sharing of data across organizations to heighten the benefits from healthcare web scraping practices.

Healthcare sector is one of the top sectors where data is multiplying exponentially with time and requires a planned and structured storage of data. Continuous web scraping and data extraction is necessary to gain useful insights for renewing health insurance policies periodically as well as offer affordable and better public health solutions. Web scraping and data extraction together can process the mammoth mounds of healthcare data and transform it into information useful for decision making.

To reduce the gap between various components of healthcare sector-patients, doctors, pharmacies and hospitals, healthcare organizations and websites will have to tap the technology to collect data in all formats and present in a usable form. The healthcare sector needs to overcome the lag in implementing effective web scraping and data extraction techniques as well as intensify their pace of technology adoption. Web scraping can contribute enormously to the healthcare industry and facilitate organizations to methodically collect data and process it to identify inadequacies and best practices that improve patient care and reduce costs.

Source: https://www.promptcloud.com/blog/why-health-care-companies-should-use-web-scraping

How to Scrape a Website into Excel without programming

How to Scrape a Website into Excel without programming

This web scraping tutorial will teach you visually step by step how to scrape or extract or pull data from websites using import.io(Free Tool) without programming skills into Excel.

Personally, I use web scraping for analysing my competitors’ best-performing blog posts or content such as what blog posts or content received most comments or social media shares.

In this tutorial,We will scrape the following data from a blog:

    All blog posts URLs.
    Authors names for each post.
    Blog posts titles.
    The number of social media shares each post received.

Then we will use the extracted data to determine what are the popular blog posts and their authors,which posts received much engagement from users through social media shares and on page comments.

Let’s get started.

Step 1:Install import.io app

The first step is to install import.io app.A free web scraping tool and one of the best web scraping software.It is available for Windows,Mac and Linux platforms.Import.io offers advanced data extraction features without coding by allowing you to create custom APIs or crawl entire websites.

After installation, you will need to sign up for an account.It is completely free so don’t worry.I will not cover the installation process.Once everything is set correctly you will see something similar to the window below after your first login.

Step 2:Choose how to scrape data using import.io extractor

With import.io you can do data extraction by creating custom APIs or crawling the entire websites.It comes equipped with different tools for data extraction such as magic,extractor,crawler and connector.

In this tutorial,I will use a tool called “extractor” to create a custom API for our data extraction process.

To get started click the “new” red button on the right top of the page and then click “Start Extractor” button on the pop-up window.

After clicking  “Start Extractor” the Import.io app internal browser window will open as shown below.

Step 3:Data scraping process

Now after the import.io browser is open navigate to the blog URL you want to scrape data from. Then once you already navigated to the target blog URL turn on extraction.In this tutorial,I will use this blog URL bongo5.com  for data extraction.

You can see from the window below I already navigated to www.bongo5.com but extraction switch is still off.

Turn extraction switch “ON” as shown in the window below and move to the next step.

Step 4:Training the “columns” or specifying the data we want to scrape

In this step,I will specify exactly what kind of data I want to scrape from the blog.On import.io app specifying the data you want to scrape is referred to as “training the columns”.Columns represent the data set I want to scrape(post titles,authors’ names and posts URLs).

In order to understand this step, you need to know the difference between a blog page and a blog post.A page might have a single post or multiple posts depending on the blog configuration.

A blog might have several blog posts,even hundreds or thousands of posts.But I will take only one session to train the “extractor” about the data I want to extract.I will do so by using an import.io visual highlighter.Once the data extraction is turned on the-the highlighter will appear by default.

I will do the training session for a single post in a single blog page with multiple posts then the extractor will extract data automatically for the remaining posts on the “same” blog page.
Step 4a:Creating “post_title” column

I will start by renaming “my_column” into the name of the data I want to scrape.Our goal in this tutorial is to scrape the blog posts titles,posts URLs,authors names and get social statistics later so I will create columns for posts titles,posts URLs,authors names.Later on, I will teach you how to get social statistics for the post URLs.

After editing “my_column” into “post_title” then point the mouse cursor over to any of the Posts title on the same blog page and the visual highlighter will automatically appear.Using the highlighter I can select the data I want to extract.

You can see below I selected one of the blog post titles on the page.The rectangular box with orange border is the visual highlighter.

The app will ask you how is the data arranged on the page.Since I have more than one post in a single page then you have rows of repeating data.This blog is having 25 posts per page.So you will select “many rows”.Sometimes you might have a single post on a page for that case you need to select “Just one row”.

Source: http://nocodewebscraping.com/web-scraping-for-dummies-tutorial-with-import-io-without-coding/

Data Discovery vs. Data Extraction

Data Discovery vs. Data Extraction

Looking at screen-scraping at a simplified level, there are two primary stages involved: data discovery and data extraction. Data discovery deals with navigating a web site to arrive at the pages containing the data you want, and data extraction deals with actually pulling that data off of those pages. Generally when people think of screen-scraping they focus on the data extraction portion of the process, but my experience has been that data discovery is often the more difficult of the two.

The data discovery step in screen-scraping might be as simple as requesting a single URL. For example, you might just need to go to the home page of a site and extract out the latest news headlines. On the other side of the spectrum, data discovery may involve logging in to a web site, traversing a series of pages in order to get needed cookies, submitting a POST request on a search form, traversing through search results pages, and finally following all of the "details" links within the search results pages to get to the data you're actually after. In cases of the former a simple Perl script would often work just fine. For anything much more complex than that, though, a commercial screen-scraping tool can be an incredible time-saver. Especially for sites that require logging in, writing code to handle screen-scraping can be a nightmare when it comes to dealing with cookies and such.

In the data extraction phase you've already arrived at the page containing the data you're interested in, and you now need to pull it out of the HTML. Traditionally this has typically involved creating a series of regular expressions that match the pieces of the page you want (e.g., URL's and link titles). Regular expressions can be a bit complex to deal with, so most screen-scraping applications will hide these details from you, even though they may use regular expressions behind the scenes.

As an addendum, I should probably mention a third phase that is often ignored, and that is, what do you do with the data once you've extracted it? Common examples include writing the data to a CSV or XML file, or saving it to a database. In the case of a live web site you might even scrape the information and display it in the user's web browser in real-time. When shopping around for a screen-scraping tool you should make sure that it gives you the flexibility you need to work with the data once it's been extracted.

Source: http://ezinearticles.com/?Data-Discovery-vs.-Data-Extraction&id=165396