RAG & LLM: 62 fewer hours of documentation work per week (outpatient care)

Artificial intelligence & healthcare

The Challenge

A large network of outpatient care services groups several facilities. Hundreds of PDF care reports, physician letters, and internal forms arrive daily. Quality management and leads had to open documents, manually transfer bullet points into systems, and summarize for teams—a process that caused frustration and overtime. Errors occurred when information was missed or duplicated. At the same time, any AI solution had to be strictly privacy-compliant and must not repurpose patient data for model training.

Our Solution

Solution impressions

Groenewold IT Solutions implemented an internal AI knowledge base with a RAG architecture: documents enter a segregated processing path, are chunked, vectorized, and queried only within defined tenant scopes. Large language models produce structured short summaries, required fields, and alerts (e.g., medication changes) that staff can confirm in a review dialog. Hosting and key management were chosen so processing can remain in the EU; access is role-based and fully traceable. AI becomes a tool for reading and structuring—not an uncontrolled chat on raw data.

Results

After rollout and training (illustrative scenario): • Roughly 62 hours per week of manual reading and summarization saved—across QM, leads, and deputy care management. • Faster handovers between shifts because core facts are visible at a glance. • Less duplicate documentation entry; review steps are auditable. • High acceptance because care staff experience AI as support, not extra burden. The solution can be extended with more document types and internal policies—without compromising client confidentiality.

Features

Feature overview

• –RAG-based processing of internal PDFs and records
• –Structured briefs and required fields with review workflow
• –Tenant- and role-based access (GDPR-oriented)
• –No repurposing of patient data for model training
• –Traceable logging of queries and approvals
• –Extensible to new document classes and policies