Medical Document Parsing
Document Classification
Overview
A pre-requisite to an effective medical document management system is to know the type of document. Identifying the type (or the class) not only helps to better perform automated parsing, but also helps the data feduciaries to effective organise this records. EkaCare’s document classification solution leverages its powerful customised vision-LLM to accurately categories a document into one of the 15+ relevant categories. Designed specifically for the Indian healthcare ecosystem our solution offers high level of accuracy and doesn’t involve human in a loop.This service offers:
- Ability to work with PDFs as well as scanned/clicked images of prescriptions
- Automated categorisation of a medical document into 15+ categories
List of relevant document categories can be obtained hereExample**
Use Cases
- Electronic Medical Record (EMR) Organize and apply smart filters to provide physicians with seamless access to historical health records.
- Personal Health Record Apps (PHR) Categorize and organize medical records for users, ensuring easy access to health history within the app.
- Insurance Claims Processing Quickly identify and classify medical documents to expedite claim verification and approvals.
Technology Deep-Dive
Our document classification service is powered by or custom Large Language Models (LLMs), specifically trained on millions of diverse medical documents. These documents span diverse formats and contexts, with a particular focus on the Indian healthcare ecosystem. Our rigorous training and fine-tuning process ensures exceptional accuracy while minimizing common pitfalls like hallucinations that often impact other SOTA LLMs. The result is a highly reliable system, as demonstrated in the benchmarks provided in the subsequent section.Evaluation and Benchmarks
Our benchmark experiments with evaluation dataset comprising thousands of documents showcase Eka’s superior performance in terms of accuracy compared to other SOTA models. NOTE this evaluation dataset contains both PDF and clicked images.| Task | Parrotlet-V (Eka Care’s LLM) | OpenAI GPT-4o | Claude Sonnet 3.5 | Qwen2-VL (7B) | Llama-3.2-Vision (11B) | Phi-3.5-vision (4.2B) |
|---|---|---|---|---|---|---|
| Classification | 0.970 | 0.838 | 0.882 | 0.810 | 0.756 | 0.752 |
Spotlight
Try Out
Experience the power of EkaCare’s document extraction with our developer-friendly API.- Visit our API Documentation to get started.
- Upload a lab report or prescription and see our technology in action.
- Contact us for a custom demo tailored to your use case.