The Power of Dexit’s AI-native Data Extraction and Classification

Picture this: your healthcare team is drowning in a sea of documents—sorting, categorizing, and manually extracting critical information from each. Managing the deluge of healthcare documents is always a challenge. From classification to extracting specific data points, these tasks are labor-intensive and prone to human error. 

However, AI automation is transforming how healthcare organizations handle document management. Using an AI-native Intelligent Document Processing (IDP) solution, processes that once took hours can get completed in seconds with remarkable accuracy.

In this blog, we’ll explore one such IDP solution - Dexit. Dexit’s AI-native Intelligent Document Processing is built with AI models, tailor-made for healthcare needs. Learn how Dexit empowers healthcare staff to focus on higher-value activities, by streamlining labor-intensive tasks such as document classification and entity extraction.

Explore:

• The Role of AI in Healthcare Document Management
• How Dexit Streamlines Document Classification
• How Dexit Excels in Entity Extraction Using AI
• How AI-powered Extraction and Classification Solves Real-world Challenges

1. The Role of AI in Healthcare Document Management

AI is rapidly reshaping the field of Health Information Management (HIM). Its ability to process and interpret massive volumes of data enables healthcare organizations in various ways. Here’s some of them:

• Improved speed: Rapid processing of documents reduces administrative bottlenecks.
• Reduced errors: AI models imitates human logic to get to the finish line, only it’s quicker and more precise every step of the way.
• Shifts staff focus to high-value tasks: Automation frees staff to concentrate on strategic and patient-focused efforts.

2. How Dexit Streamlines Document Classification

Dexit employs advanced Machine Learning (ML) models to automate document classification, ensuring high efficiency and precision. Here’s how Dexit’s AI-native system simplifies the process:

• Training ML Models: Dexit’s AI learns from datasets of 1,000+ images for each document type. These labeled datasets allow the AI to recognize and predict document types based on features such as text content, layout, and other visual elements.
• Mimicking Human Actions: Dexit fine-tunes its models to replicate the sorting actions healthcare staff would perform manually. For instance, the system adapts to multiple variations of the same document type, such as different consent form layouts.

Key Steps in Dexit’s Document Classification Process:
1. Training Phase: The model is trained using labeled images, learning to predict document types with high confidence.
2. Model Application: Once trained, the AI evaluates incoming documents, assigns them to categories, and handles variations with ease.
3. Handling Variations: By incorporating incremental learning, Dexit’s AI accommodates document variations, ensuring flexibility and robustness.

3. How Dexit Excels in Entity Extraction Using AI

Entity extraction is critical in healthcare, as precise information retrieval—such as patient names, dates, and procedure codes—ensures accuracy in records and workflows. Dexit’s AI combines Natural Language Processing (NLP) and OCR to revolutionize this process.

Here’s how Dexit’s entity extraction works:

1. Training Phase: Labeled datasets teach the AI to recognize specific fields like “patient name” or “procedure.” Internal teams manually annotate documents with bounding boxes to identify these entities during this initial training phase..
2. Complex Layout Handling with Embedded OCR Technology: Dexit’s AI preserves document layouts to maintain extraction accuracy. Layout distortions, which can cause accuracy to drop by 40-50%, are mitigated with state-of-the-art OCR.
3. Continuous Learning: As healthcare staff correct errors, Dexit’s AI integrates this feedback, boosting its accuracy to 95% or higher over time.

4. How AI-powered Extraction and Classification Solves Real-world Challenges

AI-native Intelligent Document Processing (IDP) solutions like Dexit, directly addresses some of the day-to-day real-world challenges faced by Health Information Management (HIM) professionals. They include:

1. Handling Poor-quality Documents: OCR technology ensures accurate extraction even from low-resolution or scanned documents.
2. Reducing Manual Workload: Automated classification and data extraction minimize repetitive tasks for staff.
3. Adapting to Evolving Needs: Continuous learning ensures the AI improves and adapts to new document types and variations.

Dexit’s AI-native approach to document classification and entity extraction is a game-changer for healthcare organizations. By automating labor-intensive processes, Dexit reduces errors, enhances efficiency, and allows healthcare staff to focus on what matters most—delivering exceptional patient care.