Optical Character Recognition (OCR) in Healthcare

In the ever-changing landscape of healthcare, technology plays a pivotal role in improving patient outcomes, streamlining operations, and enhancing data management. One such transformative technology is Optical Character Recognition (OCR). Leveraging OCR technology for enhanced clinical data management and AI analytics in healthcare is revolutionizing the way medical data is handled and analyzed, leading to significant advancements in efficiency and accuracy. This blog post delves into the intricacies of OCR technology, its impact on clinical data management, and its integration with AI analytics to propel healthcare into a new era of innovation.

Understanding OCR Technology

Optical Character Recognition (OCR) is a technology that converts different types of documents, such as scanned paper documents, PDF files, or images captured by a digital camera, into editable and searchable data. OCR technology recognizes and extracts text from images, enabling automated data entry and digital text processing.

OCR technology uses multiple approaches depending on the preference for speed or accuracy needed for document processing. Medical records can sometimes reach as high as 50,000 pages in length, but also have handwriting throughout, which makes them uniquely complex in requiring both speed and accuracy.

High speed versions of OCR heavily compress portions of an image and match it directly to a varying number of shapes matched to each symbol representing a character.
High compute (but more accurate) OCR systems use machine learning to handle various fonts, languages, and even handwriting by being able to evaluate a less compressed image of a letter, as well as evaluating the character in the context around it in more detail. For example, the number 0 versus the letter O may be differentiated by if the neighboring characters are numbers or letters respectively.
Advanced OCR technologies are capable of interpreting table structures and passing them on as structured output, even without context or prior knowledge of table structures.
Extremely advanced generative AI neural network frameworks are capable of drawing conclusions directly from an image containing text without an organized OCR component - however these are fairly experimental at present.

Errors in OCR

Errors do occur. The below comparison shows an early OCR system now free and open-sourced named tesseract still used by many today for its speed, however it falls short in the accuracy department. The below shows what happens when a poor OCR engine like tesseract is used on noisy or blurred text. More modern systems are capable of evaluating context and better character recognition to reach near perfect accuracy on a task like this.

But errors may be derived from more than simply poor hand writing or poor quality images. Often lines may drawn through text, or the text may be extracted in a non-sensical sequence. Convolutional evaluation of a page might cause an OCR system to read a block of text as a single paragraph for example, when it is really two paragraphs side by side. Additionally, color, font type, font size, and bold/italic information is often lost by these OCR systems which may lend value in the context of a page of data as a whole.

The Importance of OCR in Clinical Data Management

Traditional clinical data management often involves manual data entry, paper-based records, and fragmented information systems. These methods are prone to errors, time-consuming, and can lead to data inconsistencies, negatively impacting patient care and operational efficiency. Implementing OCR in healthcare brings numerous benefits:

Increased Efficiency: Automated data entry reduces the time spent on manual tasks.
Improved Accuracy: OCR minimizes human errors in data entry.
Enhanced Accessibility: Digitized records are easier to search and retrieve.
Cost Savings: Reduced need for physical storage and manual labor.
New Use Cases: Digitization permits new use cases, such as applying Natural Language Processing (NLP) and deduplication.

In medical imaging, OCR plays a crucial role by converting text embedded in images, such as X-rays, MRIs, and CT scans, into searchable data. This capability enhances the ability to annotate images, retrieve relevant information quickly, and improve diagnostic accuracy.

Case Study

Several healthcare organizations have successfully implemented OCR technology to improve their operations. Tenasol does this as a regular part of it’s operations, with a pipeline capable of processing hundreds of thousands of medical records per day for risk and quality purposes. A portion of our pipeline implements OCR prior to NLP when encountering unstructured documents such as images or PDF files. These files may often be inside of digital medical records.

The below table shows many of the use cases Tenasol has used for primarily Payer use cases once OCR has done its work.

Conclusion

Leveraging OCR technology for enhanced clinical data management and AI analytics in healthcare is a game-changer. It offers a myriad of benefits, including increased efficiency, improved accuracy, and enhanced patient care. As technology continues to advance, the integration of OCR and AI will play an increasingly vital role in transforming healthcare, making it more data-driven, precise, and patient-centric.

References

[1] Hegghammer, Thomas. "OCR with Tesseract, Amazon Textract, and Google Document AI: a benchmarking experiment." Journal of Computational Social Science 5.1 (2022): 861-882.