Streamlining patient onboarding for Fortune 500 healthcare pioneer

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Case Study

Streamlining patient onboarding for Fortune 500 healthcare pioneer

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Case Study

Streamlining patient onboarding for Fortune 500 healthcare pioneer

Enter your email to
access the full case study

Connect with Us

We'd love to brainstorm your priority tech initiatives and contribute to the best outcomes.

From Bottleneck to Breakthrough: A Case Study in GenAI-Powered Patient Onboarding

One-liner summary:

We helped a Fortune 500 healthcare leader accelerate patient onboarding by automating discharge summary processing with a GenAI system built on Mistral LLM, improving accuracy from 70% to 90%.

The Problem with the Status Quo

Manual discharge summary processing slowed onboarding and introduced inconsistencies across patient records. Ideas2IT built a secure, self-hosted GenAI solution using Mistral LLM and Retrieval-Augmented Generation (RAG) to extract onboarding-critical data, raising accuracy to 90%, reducing decision latency, and improving downstream care coordination.

Where the Gaps Were

A Fortune 500 healthcare provider was experiencing delays in onboarding patients due to manual discharge summary reviews.

Every new patient triggered a lengthy process: clinicians had to extract unstructured data like chief complaints, diagnoses, labs, and discharge conditions from free-text documents, one by one.

This approach:

Introduced inconsistency in clinical records
Delayed onboarding decisions
Burdened care coordinators and medical staff
Created risks of missed history or medication errors

To scale effectively while improving quality, the organization needed automation without compromising on clinical accuracy or PHI security.

What We Delivered

Ideas2IT deployed a GenAI-powered solution purpose-built for secure, clinical-grade onboarding automation.

Key components:

Self-Hosted Mistral LLM

We deployed Mistral in a private, HIPAA-compliant environment eliminating third-party exposure of sensitive PHI.

Retrieval-Augmented Generation (RAG)

We combined LLM generation with targeted retrieval of contextual data from internal knowledge stores, ensuring every response was domain-aligned, grounded, and fact-consistent.

Automated Field Extraction

Using tailored prompts, structured parsers, and content-aware logic, the GenAI system extracted:

Chief complaints
Diagnoses
Medical history
Lab results and vitals
Medications and allergies
Discharge condition

Structured outputs were fed directly into downstream care management systems, eliminating manual re-entry and latency.

Outcomes We Achieved

90% Accuracy in Extracted Fields
Up from 70% with manual review, more consistent, faster, and traceable.

Real-Time Onboarding Decisions
Discharge summaries processed on the fly enabling immediate care planning.

Zero Data Leakage
All PHI processed in a fully client-controlled, self-hosted GenAI environment.

Better Patient Experience
Personalized follow-ups and faster care coordination driven by clean, structured data.

Key Lessons

GenAI alone isn’t enough. Domain accuracy demands RAG, prompt tuning, and structured validation.
Self-hosted LLMs are critical for any regulated healthcare deployment.
Unstructured-to-structured conversion is one of the highest-value GenAI use cases in clinical operations.
‍Onboarding is the inflection point. Small wins here unlock bigger efficiencies across the care lifecycle.

Key Takeaways

Patient onboarding is where operational scale meets patient care. By automating this high-friction, high-stakes workflow with GenAI, we helped our client move faster, reduce errors, and raise the standard of care, without sacrificing control or compliance.