7 Automated Data Capture Methods To Streamline Your Business Processes

MHC Marketing    May 27th, 2022

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Data is everywhere. It’s buzzing on your smartphone, lingering in your inbox, stored in your filing cabinets, and waiting to be sorted in your mailroom. It’s estimated that 2.5 quintillion pieces of new data are created each day.

All that data and the different formats it comes in is a lot for a business to effectively manage. It’s an even bigger challenge to manually convert this information into something that can be read and used by computers, while still keeping track of your countless other projects and responsibilities.

Luckily, technological advances now allow data, in all its different forms, to be captured automatically. These tools are called automated data capture methods, and they’re a great way to help your company streamline its business procedures.

In this piece, we’ll discuss the problems with manual data capture and the different methods of automated data capture, so you can find the right one for your business’s needs.

Data Capture Methods

Data capture is the process of collecting and converting information, either manually or through automated means, into information that can be processed by a computer. There are a number of different methods for data capture. Which is right for any given task will depend on the type of information you’re starting with and the type of data you’d like to end with.

Manual Data Capture

Manual data capture refers to entering information into a computer by hand. It might include typing information from a handwritten form into a document or spreadsheet, manually transcribing audio, or copy-pasting information from one location to another.

Recent studies from the healthcare sector—where mistakes in manual data entry can actually be life-threatening—have found an average error rate for manual data capture of 3 to 4 percent, with text-based entries accounting for the majority of these errors. That means up to 400 errors for every 10,000 entries!.

For businesses with minimal data capture needs, manual data entry is an acceptable solution. But for any company with a significant data capture load, better solutions are needed to make sure employees aren’t taken away from core business tasks that require their knowledge and skills.

For these organizations, error-prone and time-consuming manual data capture is becoming increasingly outdated as technological tools become more readily available. Modern technology makes automated data capture methods a cost-effective option for most businesses.

Automated Data Capture

Automated data capture refers to the use of technological tools like artificial intelligence (AI) and image recognition software to convert information into a computer-usable format.

It’s much faster than manual data capture, far less error-prone, and frees up employees to focus on more important work.

A study published in The Journal of Nursing Administration tested the introduction of an automated data entry process technology to the nursing unit of a large hospital.

The researchers found that error rates dropped by 20% (effectively reducing it to zero), and 85% of nurses said that it allowed them to focus on patient care.



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7 Automated Data Capture Methods

There are a variety of automated data capture solutions, so it’s just a matter of determining the one that best fits your business’s current and future needs. Here are seven of the most common and useful methods for automated data capture:


AI is a broad term that generally refers to software that can “learn” to perform tasks based on curated or predefined data. It’s used for aspects of automated data capture like pattern recognition, and is especially vital to any “intelligent” methods that improve over time as they’re exposed to the types of documents and data that your business uses most.

For example, Intelligent Character Recognition (ICR), another method of data capture, might use artificial intelligence to learn how to read the handwriting of a person who regularly files handwritten forms, like a job site manager who prefers pen and clipboard for reports.

Artificial intelligence can boost efficiency in nearly any computer-based task, and it shows: In 2021 alone, a huge surge of AI usage across businesses created $2.9 trillion in business value and added around 6.2 billion hours of worker productivity.

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Web data capture is perhaps the most straightforward form of automated data capture: It’s the process of gathering information via forms on the internet. Many organizations have a “contact us” form on their website with fillable fields that convert directly to an email message or a form to collect email addresses for a mailing list.

Web data capture eliminates the step of filling out paper forms. Simple web-based forms can collect all sorts of data, and are used in nearly every sector, from consumer sales to tax preparation. Even in the healthcare sector, it’s becoming standard practice to have providers encourage patients to fill out pre-visit paperwork via a web-based form.


Scanning coded, data-containing images like QR codes and barcodes are also a form of automated data capture. These codes are a convenient way for people to quickly access encrypted information, especially now that apps and smartphones have eliminated the need for specialized barcode scanners.

This approachable technology is often used for tasks like inventory tracking, production batch tracking, patient tracking in healthcare, and more. It’s also increasingly used in retail and marketing contexts, for instance, allowing in-store customers to scan signage to learn more about an item for sale or to access a restaurant’s menu at their convenience.

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Optical character recognition (OCR) is the process of using technology to convert printed text into a form computers can recognize. It involves scanning or photographing a document and processing the image, often with AI, into text data. OCR can be used to streamline administrative processes, reduce errors, and make documents accessible for people with disabilities.

For example, MHC’s NorthStar solution uses OCR technology to capture data from scanned or emailed invoices and other business documents. The system can be tailored to your workflow, and can perform such functions as automatically porting the text information directly into your enterprise resource planning (ERP) system.


ICR also converts handwritten text into computer data. And it can learn as it goes—the software essentially teaches itself how to better capture information as it encounters new handwriting or unfamiliar, complicated fonts. It’s most often used to digitize handwritten notes, forms, or paper checks.

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Intelligent document recognition is a process that matches the information in a document to additional, corresponding information. For instance, an AP department might use OCR to read an invoice, then use IDR to automatically find the account number on the invoice and associate the new data with other information, like the client’s contact information. Similarly, a healthcare professional might use IDR to digitize handwritten charts.

Like ICR, IDR is “intelligent,” which means it can be taught to read and categorize documents. It’s a great tool for any applications that involve the use of consistent templates, like invoices or other forms.

Discover how MHC NorthStar leverages intelligent OCR and machine learning to handle your data capture needs!


This technology can differentiate between marked and unmarked boxes from forms and surveys. OMR is primarily used to read questionnaires, surveys, and paper ballots, but there are potential applications for it in digital forms.

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The Differences Between OCR, ICR, IDR and OMR 

While some of the aforementioned data capture methods perform similar tasks and can even be variants of each other, they also have very distinct differences and specific uses.

To elaborate, OCR is not intended to read human handwriting, however, it’s ideal for structured documents, invoices, and purchase orders. If your business primarily handles handwritten documents, then ICR might be the best solution for you.

With IDR being an enhancement of OCR, this technology captures and sorts documents into pre-set categories and can be a great solution for your business if you process insurance claim applications, tax returns, and similar documents. Finally, OMR is better suited for environments that require quick processing of surveys because it can easily identify filled and empty boxes, but not necessarily typed words or handwriting.

The subtle differences between these data capture methods prove that there are automated solutions for almost every business’ needs.


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Automated Data Capture Will Help Your Business Thrive

Managing large amounts of information can be a major challenge, especially when that information is handwritten or in some other format not readily accessible by computer. Automated data capture can simplify processes that were once tedious, manual tasks.

With advancements in technology, data capture tools are cost-effective for any size business and offer a great ROI thanks to the amount of time it saves employees. Every business is unique, so it’s important to find the right method of data capture for your size, budget, and needs.

MHC NorthStar is a powerful workflow automation and data capture solution that has applications across AP departments, healthcare, manufacturing, and more. And with MHC’s OCR technology built right in, you can digitize everything from bills and invoices to patient records.

When MHC’s best-in-class OCR is combined with a document management system, it offers access to more features, including straight-through processing, automated approval, and more. MHC NorthStar will grow with your business. And it’s cloud-based, which means implementation is quick and easy. 

Request a demo today to see what MHC NorthStar can do for your business!

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