The modern healthcare industry is inundated with data — including patient, clinical, lab, and insurance details forming an ocean of information organizations need to sift through.

Automation has made it possible to navigate and make sense of all this data, turning a massive complex resource into actionable results. In an era of tremendous worker burnout in healthcare, eliminating the rote work of manual data entry and management is crucial to relieving an already overburdened workforce and putting control firmly back in the hands of organizations to turn data into positive patient outcomes.

With the normalized practice of electronic health records (EHRs) and data capture, healthcare will become far more end-to-end efficient than ever before. Let’s break down how automated data capture works on an organizational level, and where you can start applying this transformational technology.

Data Capture: What it Is and How it Works

Data capture is a large-scale information collection process used in numerous document-heavy industries from banking to insurance and beyond. Healthcare stands to massively benefit from data capture implementation as long as the technology used is sound and can confidently protect patient security.

What is Data Capture in Healthcare?

Data capture in healthcare refers to the process of taking information from documents — patient records, healthcare forms, etc. — and converting them into a digital format for organizations to easily store and access on their own databases.

Organizations can apply document automation in healthcare to a variety of formats, but most are broken out into three structural categories:

• Structured — forms that have predetermined layouts to make data more legible for a machine to capture
• Semi-structured — forms with fixed sets of data but non-specific formats for that data, such as invoices with varying structure per vendor
• Unstructured — non-specific forms where either a human or artificial intelligence (AI) has to interpret the type and use of each piece of information

As AI evolves to be more ubiquitous in the healthcare space, health data capture has become much more advanced than ever. With numerous methods and types of information to gather, your healthcare organization can turn data into action with far greater efficiency.

A Step-by-Step Look at Data Capture

Traditional data capture follows a few simple steps that can be easily repeated every time a new set of information needs to be recorded. Originally, this process consisted of a manual scan, entry, and management of data to transfer forms into a collective database. But as AI healthcare technology becomes more prevalent, organizations need to move away from these slow, outdated methods in order to deliver the quality of care modern patients expect.

Today, the north star for successful data capture in healthcare is AI-driven IDP, or intelligent document processing. This umbrella term encompasses the most state-of-the-art automation tools that provide seamless data extraction from physical and digital documents, interpreting each unique form with little friction.

IDP allows for a repeatable, formal process of data capture, where organizations can easily extract, store, and manage data — filling less storage space and validating information faster. This ensures that captured data is immediately applicable for healthcare cases, using various forms of capture that fits your organization’s needs and structure for patient, clinical, lab, and insurance information.

Different Forms of Data Capture

The AI boom has opened the door to so many different possibilities in data capture, but which one is right for you and your organization? Let’s walk through some of the common options and see what makes the most sense for a healthcare organization to put into practice.

• Manual data entry: The simple and obsolete manual collection and input of data. Every organization uses this form of capture to some degree, but the goal is to reduce reliance as much as possible towards automation — think 90/10 rather than 50/50. To see if you’re overreliant on this outdated methodology, reach out to an OCR Gateway professional to start automating more of your processes.
• Automatic data entry: These forms of data capture can digitally read documents and automatically extract information into a database for immediate use.
  • Optical Character Recognition (OCR): This AI technology works exactly how it sounds — it can identify typefaces on a document (PDF file, structure form, etc.) and translate that information to digital storage, largely for high-volume data on similar forms. For health data capture, this could apply to anything from a HIPAA release form to an insurance claim.
  • Intelligent Character Recognition (ICR): The evolution of OCR, ICR identifies not just typeface characters, but hand-written ones as well. This expands the possibilities to let any patient data capture convert easily into accessible digital information.
  • Intelligent Document Recognition (IDR): This more sophisticated AI takes the ICR and applies it across documents to learn patterns in structure and index different forms based on type, layout, and other factors. IDR in healthcare helps an organization collate and organize individual document types submitted by patients or other third parties, letting you easily reference specific categories of forms at once.

• Digital input methods: These provide various approaches for capturing or transferring information from physical to digital storage.
  • Digital forms: Both patients and professionals can use these forms to input information directly into a system rather than using a handwritten form and having to transfer the information manually.
  • Digital signatures: These forms of approval are legally equivalent to physical signatures and make sign-off on essential payments, procedures, and more as simple as a click.
  • Barcodes and QR Codes: These digital IDs can live on forms, packaging, or other physical properties that a user can then scan to access the relevant information related to that item.
  • Optical Mark Reading (OMR): Similar to OCR and ICR, OMR specifically identifies markings on forms — like checks or X’s in boxes — indicating when certain fields are marked off by human input. These can be used in everything from voting ballots to healthcare applications like checking off patient information during a doctor visit.
  • Web Scraping: A more widespread form of data gathering, these web-crawling tools scan across the internet to gather information relevant to the user’s search parameters and gather that information into a single hub for easy access and application. Though not as common in healthcare, these are used often in the news, marketing, and other large-scale research projects.
• External data capture: These methods directly input information provided from sources that aren’t directly documented, written down, or otherwise easily transferred from text to digital storage.
  • Voice capture: Popularized by chatbots like Alexa and Siri, voice capture is a wide-ranging data capture technology that lets users speak information directly to a device to store, interpret, or act on certain information or commands. Outside of the well-known consumer use cases, voice capture can be used in certain medical applications, research studies, and more fields within healthcare.
  • Video or image capture: An innovative application of AI, video and image capture can identify and interpret patterns in visualized situations for real-time analysis. This technology is often used in various forms of safety like airport screenings and security systems at financial institutions.
  • Data from devices: Particularly useful in healthcare data capture, devices from consumer wearables (fitness trackers) to diagnostic and management tools (ECGs, glucose monitors) provide crucial health information to organizations for informing treatments and long-term planning.

Why Should You Use Automated Data Capture for Healthcare?

We can clearly see that data capture comes in a wide range of forms and applications, but automation is essential to conducting this process efficiently.

Intelligent automation in healthcare, particularly AI-driven IDP, has rapidly improved the accuracy and accessibility of data gathered from the hundreds or even thousands of documents your organization deals with regularly. Eliminating the friction and flaws of manual capture help expedite the implementation and application of healthcare data, making it far easier to identify patient issues and get to a satisfactory solution without a hiccup.

Investing in AI for healthcare data capture doesn’t just make your everyday life easier, but even revolutionizes the way you conduct business. Forget the days of human dependency and slow, outdated workflows. When you can automate healthcare data capture, your entire organizational flow will accelerate and make it immediately easier to turn over patients and drive revenue for your business.

Advantages of Automated Data Capture in Healthcare

Automated data capture has tangible, immediate effects on your business, from the way customers feel about your organization to your ability to cut costs and boost profits. Here are the most exciting examples of how AI helps you take a huge leap forward in your organization.

• Lower operational costs and more opportunities for profit: By introducing automated healthcare data capture to your organization, you immediately eliminate the time and error costs of manual input. The centralization of the information also reduces storage costs while simplifying oversight — and making it more affordable. Even better, by cutting down on your employee’s (often least favorite) repetitive tasks, you free them up to serve more patients than before, offering the opportunity for a higher customer load and more revenue possibilities for your business.
• More secure, accurate data: With AI in the driver’s seat, data capture removes the possibility of human error to ensure all information is accurate to the letter. Moreover, this highly sensitive healthcare information is securely gathered and stored to ensure that only the appropriate stakeholders have access to this data and using it ethically.
• Improved customer outcomes: At the end of the day, AI isn’t doing its job if customers feel like the service they’re receiving isn’t improving — or worse, getting more difficult to manage. Luckily, the speed with which organizations can apply automated data capture, coupled with the fact that patients themselves aren’t required to change their own processes, provides a positive impact for everyone involved.
• Optimizing accessibility and actionability for employees: Automatic data capture typically extracts and organizes information in some centralized database for maximum accessibility. Not only does this make it easier to manage that data, but it also empowers employees in a healthcare organization to find exactly what they need and apply the information for more seamless workflows. What’s more, internal teams will be able to audit data for compliance needs exponentially faster than manual processes, preventing logjams to meet regulatory requirements and giving you greater oversight of the management and use of data.

Bringing Data Capture to Your Healthcare Organization

At the forefront of the evolving fields of AI and automation, data capture technology in healthcare offers organizations a unique ability to serve patients and revolutionize the way they conduct business.

Everyone benefits — patients get faster, more accurate results and treatment strategies, employees gain the ability to focus on more in-depth tasks, and organizations vastly increase their ability to serve more customers without a loss in quality or service. AI is the future of healthcare — investing in innovative tactics like IDP will keep you ahead of your competition and far better equipped to handle the future of the industry and patient care.

Want to learn more about how AI-driven IDP can make a day-one impact on your own healthcare organization? Contact us today!