How Healthcare Data Scraping Supports Research, Compliance, and Patient Care?

The healthcare sector is one of the most data-intensive in the world, producing more than 30% of all data. Numerous sources, including medical journals, EHR systems, clinical trials, patient records, and even online patient evaluations and comments, contribute to this data. Each prescription, medical bill, diagnosis, or symptom reported adds to the information this data may provide.

Realizing the value this data holds, many leading companies, such as Meta, Apple, Amazon, and Google, have already invested in healthcare deals worth USD 6.8 billion since the start of 2020. Wondering why and how? This data has the potential to drive personalized medicine, improve treatment outcomes, detect diseases earlier, and optimize healthcare delivery on a global scale.

This write-up will explore data scraping as a method to discover the hidden potential in healthcare data. It’ll also discuss some typical applications, approaches, and concerns related to healthcare data scraping.

Read More: Cybersecurity Hygiene: Simple Practices to Protect Your Personal Data Online

The Value in Healthcare Data

Healthcare data holds significant value present in different forms—research papers, treatment plans, patient records, etc. Every piece of this information can contribute to innovation and improving outcomes by helping medicos, healthcare companies, and research organizations identify health trends, spot epidemics, and gauge overall population wellness. When used responsibly and analyzed timely, it improves decision-making, facilitates the provision of personalized treatments, and enhances resource allocation.

Types of Healthcare Data that Can be Scraped

Here are the key types of healthcare data that can be utilized:

1. Patient Records: Medical histories, lab results, and diagnoses that provide comprehensive insights into individual health conditions.
2. Claims and Billing Data: Information that helps identify cost patterns, inefficiencies, and opportunities for reimbursement optimization.
3. Clinical Trial Data: Findings from research studies that guide drug development and treatment protocols.
4. EHR and EMR Data: Aggregated patient information that streamlines care coordination and facilitates population health management.
5. Public Health Data: Data from health surveys and disease monitoring systems, enabling trend analysis and outbreak prediction.

Applications of Scraping Healthcare Data

Healthcare data can be used for several purposes, improving overall healthcare delivery, research & innovation, and patient outcomes. Let’s explore some of them in detail:

1. Medical Research

For medical research, experts require a continuous influx of large datasets with information on discoveries, effective treatment plans, and other evidence-based best practices. This data is often obtained by scraping healthcare data from credible databases like PubMed and ClinicalTrials.gov. PubMed alone adds over 1.27 million new articles annually! Moreover, ClinicalTrials has 5,15,000+ medical case studies that are frequently updated with the latest trial data.

2. Patient Feedback Analysis

By scraping healthcare data from public forums such as Healthgrades and RateMDs, professionals and businesses can get insights into genuine public comments on service quality, common complaints, star ratings, and overall treatment experience. Performing sentiment analysis on this data can, in turn, help you identify consumer preferences toward particular medical institutions or professionals, trends in patient expectations, etc.

3. Public Health Tracking

Epidemiology is another critical area where healthcare data scraping proves invaluable. Organizations can spot disease outbreaks by gathering real-time data from news websites, government portals, and social media mentions. This information gives them an early-mover advantage, allowing them to allocate resources and make better policy decisions to control the spread.

The Johns Hopkins University of Medicine’s coronavirus resource center is a live example. Since the pandemic, they have had a dedicated, publicly available data hub with information on active cases, deaths, administered vaccine doses, and the testing state. A significant number of these datasets are still updated daily by aggregating data from sources such as the CDC, government health departments, online reports, and other crowdfunded data.

4. Drug Price and Availability Monitoring

Scraping healthcare data from online pharmacies like CVS and Amazon Pharmacy offers valuable insights into drug availability and demand. Similarly, extracting data from prescription pricing platforms like GoodRx and global databases like GlobalData’s PharmOnline International (POLI) Database provides critical details on current drug prices and manufacturing costs. Businesses can use this information to identify alternative medications and optimize their supply chains.

Additionally, websites like Drugs.com offer data on medication side effects and safety warnings. Scraping this information allows for side-by-side comparisons, enabling more informed and adequate decision-making in healthcare planning and drug procurement.

5. Monitoring Healthcare Compliance

Web data scraping is also instrumental in ensuring regulatory compliance in healthcare provision. Providers can get real-time updates on drug approvals, recalls, safety advisories, and other guidelines by scraping data from authoritative websites like the FDA’s Drug Approvals and Databases section and WHO’s Health Product Alerts. You can internalize this information in healthcare systems to remove recalled or flagged medications from inventories.

How to Scrape Healthcare Data?

There are two main ways to scrape healthcare data: automated techniques and manual data extraction. Based on the complexity and volume of the data required, each method has its own advantages, processes, and use cases.

Using Automated Data Scraping Techniques

This approach involves using tools and custom web scraping scripts to extract data from online sources. It is mostly used for large-scale data requirements where speed and consistency are important.

This can be done by following the steps below:

• Select the target website or platform you want to scrape data from.
• Choose a data scraping tool or develop custom scripts or APIs to extract target data. Scrapy, Octoparse, and Beautiful Soup are some widely used tools.
• Set up scraping rules to target specific data fields (e.g., drug names, prices, patient reviews).

Many websites rely on anti-scraping protocols to prevent unauthorized data extraction. You must follow their guidelines and stay within extraction limits, as non-compliance can result in legal repercussions or blocked access to the website.

The famous 2022 lawsuit against Meta is a classic example. The company was accused of violating patient privacy by collecting data using its Meta Pixel tool, which was present in 30+ healthcare institutions at that time. Even though the case was dismissed after the plaintiff acknowledged Meta’s terms of service, it underscores the need for caution when scraping sensitive healthcare data.

Manual Data Extraction

This approach involves collecting information by directly visiting the websites and copying the required details. However, it is much slower and only suitable for small-scale or highly specific, one-off data collection tasks. Manual data extraction is also required when human judgment is necessary to avoid errors or situations where scraping tools cannot access the data needed due to technical restrictions.

Challenges in Scraping Healthcare Data

Although healthcare data extraction has many advantages that support medical research and enhance patient care, it also has certain drawbacks. Here are some difficulties and things to keep in mind if you want to scrape medical data:

1. Breach of Terms of Service: Most websites, including healthcare platforms, have terms and conditions that disallow scraping or limit the amount of scrappable healthcare data. Violating these restrictions (even if unknowingly) can result in temporary or permanent bans from accessing the site.
2. Privacy Laws and Regulations: Patient data is highly sensitive and protected by laws like HIPAA (Health Insurance Portability and Accountability Act). Scraping this data without consent or outside the permitted amount can result in hefty penalties.
3. Consent and Transparency: Adhering to all data scraping ethics in healthcare is crucial. Using patient data for any purpose requires explicit consent and a clear agreement on how the data will be used.
4. Technical Challenges: While automated scraping offers speed and scalability, it also has technical obstacles that can complicate the process. There could be anti-scraping mechanisms, dynamic content, or inconsistencies in formats, making it challenging to stick to one extraction technique.

Combining Automation and Human Judgment to Overcome Healthcare Data Scraping Challenges

Scraping healthcare data can be complex due to technical, ethical, and compliance challenges. However, combining automated and manual methods is a proven way to avoid these hurdles effectively.

While automated data scraping is highly efficient for large-scale extraction, it may not always suffice when websites employ anti-scraping mechanisms like CAPTCHA, rate limiting, and IP blocking. You may have to rely on techniques like IP rotation and agent spoofing or use headless browsers to scrape data in such scenarios.

Moreover, even if you bypass these mechanisms, not all website data will be relevant to your use case. Automated tools often scrape data without considering its relevance, and they can sometimes extract irrelevant or sensitive information, such as personally identifiable details (PII), going against privacy regulations.

This is where human judgment comes in handy. They ensure your efforts align with your data scraping requirements and identify specific data points that you need to scrape. They’re also more equipped to handle sensitive information, ensuring you don’t go against any data privacy laws. Moreover, their nuanced understanding is irreplaceable for context-sensitive data, such as analyzing sentiment in patient reviews.

The conclusion is that while automation offers speed and scale, human intervention ensures accuracy, compliance, and context, making the combined approach both effective and efficient.

Note: If you find it challenging to scrape healthcare data with this expansive approach, you can seek professional help from data scraping service providers. These providers have automated workflows and dedicated teams who offer end-to-end assistance, from scraping data to healthcare research data management. You will benefit from a balanced approach of automated and manual extraction methods with them.

Final Thoughts

When used responsibly, healthcare data has much potential and value that can transform medical research and patient care. This data (and its potential) is growing without bounds, especially due to the expanding population, evolving diseases, and novel treatments. However, the real value of healthcare data is found in extracting and using it ethically without violating laws and user privacy. This will not only drive innovation and enhance patient care but also create long-term value for healthcare organizations, patients, and the broader medical community.