How to Automate Data Extraction in the Healthcare Sector

There’s data everywhere in the healthcare industry – from patient registration forms and insurance claims to lab results and prescriptions. Yet, as healthcare systems become increasingly digitized, organizations still rely on manual data entry, translating into errors, inefficiencies, and compliance risks.

Manual processing slows down patient care, leads to increased billing errors, and makes it difficult for clinicians to fetch that information. Moreover, healthcare providers are required to operate under strict regulations like HIPAA and GDPR, so keeping the data secure is a top priority. Errors in medical charts or insurance claims can result in financial loss, delays in treatment, and even litigation.

Automated data extraction solutions transform how healthcare providers, insurers, and BPOs handle documents. By using AI-powered OCR solutions and Intelligent Document Processing (IDP), organizations can streamline workflows, improve accuracy, and ensure compliance – all while reducing administrative costs.

In this blog, we’ll explore how data extraction automation can solve these pain points and help healthcare organizations enhance efficiency, security, and patient care.

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Key Takeaways

• Manual data entry slows healthcare operations – Processing records, claims, and reports manually causes errors, inefficiencies, and compliance risks, affecting patient care and finances.
• Automation simplifies compliance – AI-powered tools anonymize data, enforce security, and ensure HIPAA, GDPR, and other regulatory adherence.
• Less manual work means lower costs – Automating workflows cuts labor costs, boosts efficiency, and lets staff focus on patient care.
• AI streamlines healthcare data – Solutions like Klippa automate data extraction, validation, and integration, reducing admin work for providers, insurers, and BPOs.

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What is Data Extraction in Healthcare

Data extraction in healthcare refers to the process of capturing, structuring, and processing information from documents like medical records, insurance forms, prescriptions, and lab results. 

With so many documents being handled every day, extracting data efficiently is critical for the healthcare industry. There are two primary approaches to healthcare data extraction: manual and automated.

Manual data extraction

Manual data extraction involves human operators reviewing documents and manually entering relevant information into systems such as electronic health records (EHRs), billing platforms, or insurance databases.

Despite being slow, labor-intensive, error-prone, and risky for compliance, some healthcare organizations still use manual extraction for complex or non-standardized documents that automation struggles to process. However, the increasing demand for speed, accuracy, and efficiency needs a shift toward automated solutions.

Automated data extraction

Automated data extraction uses technologies like Optical Character Recognition (OCR), Artificial Intelligence (AI), and Natural Language Processing (NLP) to extract, process, and classify healthcare data with minimal human intervention.

While OCR is used to convert printed or handwritten documents into machine-readable text, AI and NLP enable a deeper understanding of complex, unstructured data, such as doctors’ notes or lab reports. 

Machine learning (ML) can also improve the accuracy of data validation and document classification by learning from historical data. This combination of technologies allows healthcare organizations to automate the extraction of key data, allowing them to focus more on patient care and decision-making.

Now that we have covered the key technologies used, what are the typical documents and data appropriate for automated data extraction? In the following section, we’ll discuss the most important ones, what they do, and how their digitalization may ease the workflow.

What data can be extracted from medical documents?

What types of data can be extracted in the healthcare industry? Let’s explore:

• Patient Records: Include essential data like name, age, contact information, a full medical history, prior treatments, surgeries, medications, and doctor’s remarks. Such records help doctors make decisions about how to deliver continuity of care.

• Insurance Claims & EOBs:  Include charges for healthcare services, supporting insurance claims, and managing finances. Automating this data extraction speeds up claim approvals, reduces mistakes, and optimizes payment processing.

• Lab Reports & Prescriptions: Disclose a patient’s health condition through results from tests, blood work, and other diagnostics. Digitizing this data allows for quicker analysis and decision-making regarding treatment.

• Clinical Trial Data: Obtained from studies, such data are valuable for experimenting with new drugs and ensuring they are safe to administer and effective.

• Regulatory Compliance Documents: Include policies, procedures, and records showing compliance with legal and industry standards. Properly managing these files helps healthcare organizations maintain compliance and avoid penalties.

Automating information extraction from these documents reduces errors, boosts efficiency, and enhances patient care in healthcare organizations. Unfortunately, this doesn’t come without challenges. 

Challenges in Manual Data Processing

As explained above, healthcare organizations work daily with a great deal of data, from patient records to billing data. However, manual data entry can bring inefficiencies that impact administrative operations as well as patient care.

Here are some of the most important issues healthcare providers face while manually processing data.

1. Time-Consuming Workflows

Administrative staff spend hours typing patient data, processing insurance claims, and retrieving records. This leads to delays in patient treatments and inefficiency.

2. High Risk of Human Errors

Manual entry errors – for example, the wrong billing code or misspelled medical history – can lead to financial loss, claim rejections, or even medical errors that put patients at risk.

3. Compliance and Security Risks

Healthcare providers must comply with HIPAA, GDPR, and other regulations, handling data securely. Manual processing creates a higher risk of data breach and non-compliance.

4. Rising Operational Costs

Manual processes require more staff and time, leading to higher labor costs. Healthcare organizations need cost-effective solutions to handle rising amounts of data.

With growing demands for quicker and more accurate healthcare services, organizations must find ways to enhance their processes without compromising security and compliance.

It’s here that automated data extraction comes in handy. With AI-driven solutions, healthcare organizations can make use of the many benefits it presents.

Benefits of Automated Data Extraction

To stay compliant and competitive while improving patient care, many providers favor automated data extraction. This reduces inefficiencies by using AI-powered OCR to process documents faster and more accurately. Here’s how it helps:

1. Improved Accuracy & Faster Processing

An AI-powered OCR solution eliminates human errors through accurate text extraction and structured data from documents. This leads to fewer claim rejections, correct patient files, and faster billing cycles.

2. Cost Savings & Improved Efficiency

Automating data extraction from medical documents reduces administrative time, allowing employees to focus on more valuable work like patient care and claim processing. All while saving operational costs.

3. Compliance & Data Protection

Automated systems anonymize sensitive data, encrypt information, and track document revisions so they comply with health legislation.

4. Seamless Integration with Healthcare Systems

Intelligent document processing technologies integrate EHR systems, billing platforms, and insurance databases, computerizing departmental processes.

By integrating AI-powered solutions, healthcare organizations can not only automate but also attain new levels of efficiency and security. The advantages of automation go beyond individual workflows and into overall data management strategies that create better patient outcomes and business success.

How to Automate Data Extraction from Medical Documents 

Considering all the benefits of data extraction automation, no wonder you would like to find a solution that is customizable for your type of institution and the number of documents you handle on a daily basis. 

We know that there are endless possibilities to choose from, but the one we can wholeheartedly recommend is Klippa’s!

Klippa DocHorizon is an advanced Intelligent Document Processing (IDP) platform that streamlines document workflows with ease. It supports multiple document types and formats, making it highly versatile for various use cases like data extraction from medical documents.

Curious to see it in action? Let’s show you the process step by step. And the best part? You can try it for free!

Step 1: Sign up on the platform

First things first! Sign up for free on the DocHorizon Platform. Just enter your email and password, then fill in details like your name, company, use case, and document volume. 

Once registered, you’ll receive €25 in free credits to explore the platform’s features. Sweet, right? There is no need for any type of commitment, and, most importantly, you can see for yourself if Klippa DocHorizon is the right solution.

After logging in, create an organization and set up a project to access the services. For now, to extract data from medical prescriptions, you need to contact Klippa’s support team to enable this function for you! Rest assured, Klippa is working to make this a publicly available service, so keep an eye out for Klippa’s blog!

Now that the support team has enabled this function, you can select the Medical Prescription Model and Flow Builder. And you are ready to go!

Step 2: Create a preset

The model you’ve selected is built to streamline workflows by automating data extraction, analysis, validation, and classification.

Once activated, create a new preset – let’s call it “Data Extraction from Medical Documents.” This allows you to enable the necessary components for your use case. 

Almost there! Click “Save” to finalize your settings, and you’re ready for the next step in the Flow Builder.

Step 3: Select your input source

After setting up your preset and enabling the Flow Builder, it’s time to create your flow — a sequence of steps that determines how invoices are processed and delivered to their destination. For this example, we’ll use the Inbox of your email address as the input source.

Step 1: Create a New Flow

1. Navigate to the Flow Builder in the Services area.
2. Click New Flow → + From Scratch and assign a name. Let’s call it “Medical Data Extraction.”

Step 2: Select and Test Your Input Source

The first step in your flow is choosing where your invoices will be sourced. You can upload files manually or connect to over 100 external sources, including Dropbox, Outlook, Salesforce, Zapier, OneDrive, Amazon S3, iCloud, and company databases.

Tip: To process documents in bulk, place all files in the same folder.

For this example, we’ll:

• Choose Inbox: New Email as the input source.
• Select the Connection as Default DocHorizon Platform.
• A Test Address was created. We’re going to send an email to this address, having as an attachment the medical prescription from which we would like to extract data.
• Click on Test trigger.

After this step is successfully tested, we can move to the next step in the process! 

Step 4: Capture and extract data

Now, it’s time to extract the required data using the preset you created earlier. This will process all selected data fields from the invoices in the input folder.

Steps in Flow Builder:

1. Click the + button and select Document Capture: Medical Prescription.
2. Configure the following settings:
   • Connection: Default DocHorizon Platform
   • Preset: Your preset name (e.g., “Data Extraction from Medical Documents”)
   • File or URL: Inbox: New Email attachments 0 file

Test this step to confirm everything is functioning correctly. Once the test succeeds, you’re ready for the next step: saving your results!

Step 5: Save the file

After extracting the invoice data, the final step is to define the destination and output format. The destination can be a database, ERP system, accounting software, or any other platform based on your workflow. Supported output formats include JSON, XML, CSV, XLSX, PDF, and TXT.

Example Setup:

In this example, we’ll save the extracted data in JSON format, using the invoice number as the file name. The output will be stored in a newly created Google Drive folder named “Output”.

Steps in Flow Builder:

1. Click the + button and select Create new file → Google Drive.
2. Configure the following settings:
   • Connection: google-drive
   • File Name: Document Capture: Medical Prescription → components → prescription, followed by .json
   • Text: Document Capture: Medical Prescription → components
     • Tip: Selecting “components” ensures all extracted elements are included in the document.
   • Content Type: Text
   • Parent Folder: Output (your designated output folder)

Finally, test this step by clicking the button at the bottom right. Successful? Then that was it! Now, you know the simplest way in which you can save time without giving up either accuracy or efficiency! 

Who Benefits from Klippa’s Data Extraction in Healthcare?

Efficient data management is crucial in healthcare, where speed, accuracy, and security matter. Below are just a few examples of how Klippa helps professionals in the industry streamline their workflows.

Persona	Main Pain Points	How Klippa Solves It
Hospital/Lab IT Manager	Data security, manual entry inefficiencies, slow retrieval	Automates data input, integrates with EHR, ensures compliance
Project Manager/External Relations Officer	Insurance claim rejections, billing errors, slow payments	Extracts billing data, prevents rejections, and accelerates claims
Healthcare BPO Manager	Labor costs, accuracy issues, scalability	Reduces manual work, improves accuracy, increases efficiency
Compliance Officer	Regulatory risks, manual audits, data security	Automates compliance reporting and enhances data security

Of course, these are just some of the many use cases Klippa can support. For example, Klippa helps Antwerp University Hospital (UZA) process expense claims up to 3x faster, reducing manual work and improving efficiency. If you have specific challenges, our team is happy to explore solutions tailored to your needs.

Go Beyond Automated Data Extraction with Klippa 

Healthcare should be about caring for patients, not drowning in paperwork. Automated data extraction takes the burden off of healthcare providers, insurers, and BPOs by eliminating manual data entry, reducing errors, and speeding up processes. The result? Faster billing, fewer mistakes, and more time to focus on what really matters – patient care.

With Klippa DocHorizon, you get an AI-powered solution that simplifies document handling while keeping data secure and compliant. Key features of Klippa’s solution for businesses in the healthcare industry:

• High-accuracy OCR: Extract data fields from various document types, such as patient records, prescriptions, and invoices, with high accuracy.
• Data anonymization: Automatically redact sensitive information to comply with privacy regulations.
• Seamless integration: Connect effortlessly with existing EHR systems, insurance platforms, and billing software via SDK or API.
• Fraud prevention: Authenticate documents and detect fraudulent activity with automated verification.
• Advanced classification: Organize and categorize documents based on content and specific data fields.

Take the next step in automation – contact our experts for additional information or book a free demo below!

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