RAG & LLM: 62 fewer hours of documentation work per week…

Artificial intelligence & healthcare

The Challenge

PDF flood and manual summarization

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. Shift handovers lost time because core facts were buried in long documents.

Privacy as a hard constraint

At the same time, any AI solution had to be strictly privacy-compliant and must not repurpose patient data for model training. Public chat tools or undocumented cloud services failed on compliance and team trust.

“We want time for people—not walls of PDFs. But without control and EU hosting, no AI enters our daily work.”

Acceptance only with review, not autopilot

Care staff should feel relief without machine text landing in records unreviewed. Review workflow, roles, and traceability were mandatory—not optional.

Quality management needed proof of which documents were processed when and approved by whom.

Our Solution

Solution impressions

RAG knowledge base in a segregated path

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. See AI knowledge base, AI implementation; topics hub AI.

Review dialog, EU hosting, governance

Staff confirm briefs in a review dialog; medication changes and alerts are not applied without approval. 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.

“AI pulls facts from the files—we review and sign off. Relief with control—exactly what we need in healthcare.”

Extensibility and training

New document classes and internal policies can be added without compromising client confidentiality. Training focused on review instead of prompt play—acceptance rose measurably.

Made in Germany: design and delivery by Groenewold IT Solutions in Leer/East Frisia.

Results

62 fewer hours of reading care per week

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 entry, high acceptance

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.

RAG pipeline and tenant separation

Ingestion, chunking, vectorization

PDFs and internal forms pass through a segregated processing path; chunks are indexed per tenant. Queries run only within defined permissions.

No repurposing of patient data for model training—processing on contract, EU hosting.

Structured output and alerts

The LLM delivers required fields, short summary, and alerts (e.g. medication changes); format is consistent for care systems and QM lists.

Review, logging, and rollout

Review dialog and traceability

Approvals are logged; who confirmed which brief when is auditable. No write to productive record fields without approval.

Training and phased document classes

Rollout started with care reports and physician letters; more document types followed after validation with QM. Teams were trained on “review instead of retyping”.

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

Frequently asked questions about AI-assisted documentation in ambulatory care

Where does AI concretely reduce documentation effort in ambulatory care?

Mostly in progress notes, service records, handovers, and the structuring of recurring free-text entries. Combining an AI knowledge base with app development creates mobile workflows that fit directly into field care routines.

How can AI documentation remain GDPR-compliant in care settings?

The essentials are role-based access, explicit storage rules, minimal data use, and traceable logs. With software development and AI costs, you can plan a setup that balances compliance, clinical practicality, and budget.

Can AI also work with care standards and internal operating guidelines?

Yes, if guidelines, forms, and internal standards are maintained as a controlled knowledge base. This is exactly where an AI knowledge base helps by grounding responses in official rules and internal workflows instead of uncontrolled free-text patterns.

How do you start a care documentation project with low implementation risk?

A good starting point is a pilot focused on one documentation type, a small team, and only a few integrations into the care platform. Examples from AI projects and mobile app development help define a realistic rollout scope.

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