How to Extract Health Insurance Card Data and Send It to Any Target
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| Workflow Name: |
Extract Health Insurance Card and Send It to Any Target |
|---|---|
| AI Model Type: |
Vision / LLM |
| Model Provider: |
Goldfinch AI / OpenAI |
| Task Type: |
Insurance Data Extraction |
| Input Type: |
Image / PDF |
| Output Format: |
JSON / CSV |
| Who Uses It: |
Healthcare Ops; Insurance Teams |
Table of Contents
Description
| Problem Before: |
Manual insurance card processing |
|---|---|
| AI Solution: |
OCR + insurance entity extraction |
| Validation (HITL): |
Sampled QA review |
| Accuracy Metric: |
Field-level accuracy % |
| Time Savings: |
85% faster intake |
| Cost Impact: |
Reduced admin overhead |
Extract Health Insurance Card and Send It to Any Target
This workflow enables Health Insurance Card Extraction from scanned cards or PDFs using vision and LLM models.
Automated Insurance Data Processing
The system captures member details, policy numbers, and insurer information, converts them into structured JSON or CSV, and sends the data to any target system. It helps healthcare operations and insurance teams reduce manual entry, improve accuracy, and accelerate downstream processes.
Watch Demo
| Video Title: |
Insurance Industry Document Automation Use Cases, Data Flow |
|---|---|
| Duration: |
5:06 |
Outcome & Benefits
| Accuracy: |
97% |
|---|---|
| Touchless Rate: |
82% |
| Time Saved: |
From 4m to 40s/card |
| Cost Saved: |
$0.45 per card |
Functional Details
| Business Tasks: |
Insurance verification |
|---|---|
| KPI Improved: |
Intake speed; accuracy |
| Scheduling: |
Batch / Real-time |
| Downstream Use: |
Datalake / Claims Systems |
Technical Details
| Model Name/Version: |
GPT-4o-mini Vision |
|---|---|
| Hosting Type: |
Secure Cloud API |
| Prompt Strategy: |
Insurance schema-guided prompts |
| Guardrails: |
PHI masking; access controls |
| Throughput: |
70 cards/min |
| Latency: |
~2s/card |
| Data Governance: |
HIPAA-aligned handling |
FAQ
1. What is the Extract Health Insurance Card and Send It to Any Target workflow?
It is an AI-powered workflow that uses vision and LLM models to extract structured insurance information from health insurance cards and send it to any target system.
2. How does the workflow work?
The workflow ingests health insurance card images or PDFs, applies vision and LLM models to extract key insurance data, and exports it in JSON or CSV format to the configured target system.
3. What information can be extracted from health insurance cards?
It can extract details such as patient name, policy number, plan type, insurer name, group number, effective dates, and other relevant insurance metadata.
4. How is sensitive insurance data handled?
The workflow applies privacy and security measures, including access controls, encryption, and compliance with healthcare regulations, to ensure secure handling of sensitive insurance data.
5. What is the output of the workflow?
The extracted insurance card data is output in JSON or CSV format and can be sent to EHRs, insurance platforms, Datalakes, or other authorized systems.
6. Who uses this workflow?
Healthcare Operations Teams and Insurance Teams use this workflow to automate insurance card processing, improve accuracy, and accelerate patient verification and claims processing.
7. What are the benefits of automating health insurance card extraction?
Automation reduces manual data entry, ensures accurate insurance data capture, speeds up verification processes, and supports seamless integration with downstream healthcare and insurance systems.
Resources
Case Study
| Industry: |
Healthcare / Insurance |
|---|---|
| Problem: |
Slow eligibility checks |
| Solution: |
AI insurance card extraction |
| Outcome: |
Faster patient onboarding |
| ROI: |
2-month payback |

