How to Use AI to Extract Data from Claim Submissions at Scale

For most health insurance companies, processing claim documents has long been a bottleneck requiring manual data extraction from various document formats including PDFs, scanned documents, and digital forms.

In fact, a 2022 Accenture survey found that up to 40% of insurance underwriters’ time is spent on administrative activities, including manual data extraction. This process is not only time-consuming but also highly prone to human error, with claims-processing error rates historically exceeding 15%.

Health insurance claims typically contain a complex mix of structured and unstructured data: member and policy IDs, handwritten answers, provider NPI numbers, explanation of benefits (EOB) details, coverage determinations, clinical notes, scanned attachments, and more.

How Healthcare Can Benefit from LLMs

LLMs can dramatically streamline health insurance claim processing by handling complex, unstructured documents automatically in seconds rather than hours. They can:

Automate claims processing by extracting and validating key fields
Draft and review prior authorization decisions
Summarize medical records for faster underwriting or adjudication
Detect fraud, waste, and abuse through pattern recognition
Power chatbots for member support and plan navigation
Ensure regulatory compliance through document audits

Our insurance customers report increased extraction accuracy of up to 20%, along with improved efficiency compared to traditional methods. As a result, Reducto enables providers to focus more time on high-value activities like data analysis, decision-making, and patient support.

Claim Form Examples with Parsing Challenges

Many health insurance claim forms can be particularly tricky documents for LLMs as they seek to extract data to support more efficient claim fulfillment and policy analysis for insurance providers. See below for some representative examples:

A complex layout of input boxes, checkboxes, and tables that challenge both LLMs and traditional OCR.
It’s hard to distinguish template prompts from user-entered data—especially with messy handwriting and inconsistently checked boxes.
While LLMs can extract data quickly, even small inaccuracies are unacceptable in healthcare. If outputs can’t be trusted, insurers lose efficiency to manual review.

UB-04 – Inpatient and Emergency Room Claims

Handwritten clinical notes in open fields
Variability in how diagnosis and procedure codes are annotated
Scanned attachments in inconsistent orientations

NCPDP Universal Claim Forms – Pharmacy Claims

Tightly packed boxes for NDCs, member IDs, DOB, and contact info
Multi-column layouts that disrupt reading order
Fax artifacts and blurring that hinder OCR

CMS-1500 – Durable Medical Equipment (DME) Claims

Dense clustering of Healthcare Common Procedure Coding System (HCPCS) codes, modifiers, and descriptors
Overlapping entries due to manual edits
Form variants across suppliers complicate processing

Demonstrating How Reducto’s Parsing Can Help

Reducto is an AI-powered document processing platform that combines cutting-edge vision models and LLMs to deliver highly accurate, reliable data extraction. It intelligently interprets document layouts and uses specialized pipelines for each content type—ideal for processing complex, unstructured files like insurance claims at scale.

Let’s use the intake form above as an example in the Reducto Playground, where our API output can be tested live.

Let’s say you need to ingest thousands of health insurance claim forms and make them searchable. Reducto acts as your automated document ingestion engine—delivering structured, accurate data that’s ready for indexing and querying.

Despite the complexity of the document structure, Reducto’s Parse API preserves the original layout, maintains a logical reading order, and captures critical elements like checkboxes. Check out the sample output in our Playground.

If you only need to extract specific fields—like the insured patient’s name, address, or birthday—you can use Reducto’s Extract API to return a clean, targeted JSON. This schema can then be reused and applied across thousands of the same forms. Check out the sample output in our Playground.

This accuracy is made possible by Reducto’s multi-step pipeline, which combines traditional OCR, vision-language models, and heuristic rules to segment and extract each page with precision.

Unlock Faster, More Accurate Claims Processing with Reducto

Health insurance claims are among the most complex, error-prone documents to process. Traditional OCR tools struggle with handwritten notes, dense tables, and inconsistent layouts—leading to delays, errors, and manual rework.

Reducto changes that. By combining vision models, LLMs, and purpose-built parsing pipelines, Reducto delivers clean, structured data from even the messiest claims—reducing manual effort and boosting accuracy by up to 20%.

Whether you want to automate ingestion, speed up adjudication, or enable downstream analytics, Reducto helps you scale document understanding with confidence.

👉 Try your own documents in the Playground