Solving Healthcare Interoperability with llms.txt

Our mission is to transform healthcare communication by creating a universal language layer powered by large language models, making healthcare data accessible, understandable, and actionable for everyone.

The Healthcare Interoperability Problem

The Challenge in a Nutshell

•Siloed Systems: EMRs (Epic, Cerner), billing systems, and insurance platforms operate in isolation with limited communication capabilities.
•Format Fragmentation: Data exists in multiple formats (HL7, FHIR, custom JSONs, PDFs) with no universal translator.
•Manual Processes: Healthcare still relies heavily on faxing, phone calls, and printed records for critical information exchange.
•Communication Gaps: Interactions between providers, payers, and patients remain slow, unclear, and inconsistent.

Despite decades of standardization efforts and billions in technology investments, healthcare remains plagued by communication barriers. Patients struggle to understand their care plans, providers waste hours on administrative tasks, and organizations lose efficiency through manual data reconciliation.

The result? Higher costs, medical errors, patient frustration, and provider burnout. Traditional approaches to interoperability have focused on rigid standards and complex integrations that are expensive to implement and maintain.

How llms.txt Helps: Prompt-Powered Interoperability Layer

LLMs as Transformers, Explainers, and Routers

The llms.txt standard creates a new approach to interoperability by leveraging large language models as universal translators between healthcare systems, formats, and stakeholders.

Rather than requiring complex point-to-point integrations, llms.txt establishes a standardized way for systems to communicate through AI-powered prompts that can transform, explain, and route healthcare information.

The Technical Approach: Prompt-Driven Interoperability

# PROMPT: claim-explanation
[metadata]
version: 1.0
author: LLMS.healthcare
description: Transforms complex EOB data into patient-friendly explanations
input_format: JSON
output_format: plain_text

[system]
You are a helpful healthcare assistant explaining insurance claims to patients.
Explain the following EOB (Explanation of Benefits) in simple terms.
Focus on what the patient owes and why. Use plain language a 6th grader would understand.

[user]
{{EOB_DATA}}

[model]
gpt-4o

How It Works

1
Prompt Registry: Each prompt in llms.txt represents a unit of LLM logic (translate, summarize, extract) with clear inputs and outputs.
2
Format Flexibility: Prompts can ingest structured (FHIR, HL7) or unstructured (PDF, EOB) input and transform it into any required format.
3
Intelligent Routing: Responses are routed to patient-facing channels like SMS, email, or dashboards based on context and preferences.
4
Continuous Improvement: Prompts can be versioned, tested, and refined over time to improve accuracy and effectiveness.

How It Helps Patients: Real-World Examples

See how llms.txt transforms complex healthcare information into actionable insights

Example 1: Claim Explanation

Before:

Patient receives a dense EOB (Explanation of Benefits) with CPT codes, adjustment amounts, and benefit calculations they don't understand.

After:

Message from your health plan:

"You were billed $75 for your doctor visit on June 12. You haven't met your deductible yet, so you owe this amount. You've now paid $450 toward your $1,000 annual deductible."

Example 2: Appointment Summary

Before:

Patient receives a complex FHIR document as a discharge note with medical terminology and no clear next steps.

After:

Your visit summary:

"Dr. Lee diagnosed you with a sinus infection. She recommends:
• Rest and drink plenty of water
• Take the prescribed antibiotic for 10 days
• Follow up in 7 days if symptoms don't improve
• Call 555-1234 if you develop fever or worsening headache"

Example 3: Missing Documents Reminder

Before:

Patient gets a generic portal notification about "additional documentation required" with no specifics.

After:

SMS from Memorial Hospital:

"Hi Maria! We're reviewing your insurance claim for your MRI. To finish processing, we need a copy of your referral letter from Dr. Johnson. You can upload it through our patient portal or reply to this message for other options."

Example 4: Medication Instructions

Before:

Patient receives standard medication information with complex medical terminology and generic instructions.

After:

Your personalized medication guide:

"Your new blood pressure medication (lisinopril) works best when taken:
• Every morning with breakfast
• At the same time each day
• Even if you feel fine

Common side effects include dizziness when standing up quickly. This usually improves after the first week. Call your doctor if you develop a dry cough that doesn't go away."

Whitepaper: LLM-Powered Interoperability

LLM-Powered Interoperability: Using Structured Prompts to Bridge Healthcare Systems

A technical whitepaper on the llms.txt approach

Abstract

The llms.txt format is a lightweight, human-readable prompt registry that enables LLMs to act as real-time translators, routers, and explainers across disconnected healthcare systems. By structuring prompts using [system], [user], [model], and [metadata] blocks, this method allows healthcare organizations to build scalable automation flows that transform data between formats (e.g., FHIR to plain text), auto-generate patient messages, and explain billing/claims info without needing to deeply integrate siloed platforms.

Key Sections

•The Current State of Healthcare Interoperability
•LLMs as Universal Translators: A New Paradigm
•The llms.txt Format: Technical Specification
•Implementation Case Studies and Results
•Security, Privacy, and Compliance Considerations
•Future Directions and Research Opportunities

Contact LLMs Healthcare

We're building a community of healthcare organizations, technologists, and patient advocates committed to making healthcare data more accessible and actionable through LLM-powered interoperability.