AI-powered EHR: Automate Clinical Documentation and ICD-10 Coding

Problem: Physicians Spend Too Much Time on Documentation

Physicians spend over one-third of their working hours on clinical documentation — confirmed by American Medical Association studies. EHRs are overloaded with copy-paste, templated text, and irrelevant data. Clinical value is lost in noise, and manual ICD-10 coding eats hours. In one clinic, we implemented an NLP pipeline that reduced charting time from 3 hours to 45 minutes per day. Our pipelines and LLM models save physicians up to 4 hours daily on documentation with entity extraction accuracy of at least 88% (F1-score).

How AI Solves the Copy-Paste Problem in EHR

The NLP pipeline automatically detects duplicates and conflicting entries (e.g., different dosages of the same medication). The model compares semantic similarity of entries and flags anomalies. The result is a clean, structured clinical profile without noise. The system also identifies contradictions in patient histories, critical for patient safety.

How We Ensure Medical Data Security

To meet HIPAA and GDPR requirements, we offer on-premise deployment. Data is encrypted in transit and at rest, access is restricted via OAuth2 with role-based access control. The system can be certified as medical software, including audit logs and 21 CFR Part 11 compliance.

AI Features for EHR

Automated Structuring of Clinical Notes

The NLP pipeline extracts structured data: diagnoses (with ICD-10 codes), symptoms (with modifiers), medications, lab values, exam results. We use fine-tuned ClinicalBERT and specialized NER models. Accuracy: F1 0.88–0.94. Operational cost savings on documentation reach 40%.

Ambient Clinical Documentation

A voice assistant based on ASR (Whisper) and NLP automatically generates SOAP-format clinical notes. The physician verifies the AI-generated text. Savings: 1.5–2.5 hours per day. Contact us for a consultation on implementing this technology in your clinic.

Automatic ICD-10/ICD-11 Coding

An ML multi-label classification model (HiLAP) maps notes to codes. Trained on 50,000 labeled cases, we achieved 92% accuracy on a test set. AI coding is 20 times faster than manual. The project pays for itself in 6–12 months by saving physician time. Get in touch for a demonstration on your data.

Clinical Summarization

For patients with multi-year histories, an LLM (fine-tuned GPT-4) generates a structured summary: main diagnoses, medications, recent exams. We use RAG to reduce hallucinations. On-premise deployment is available.

Duplicate and Conflicting Information Detection

NLP identifies duplicates and contradictions (e.g., different dosages) — critical for patient safety.

How We Configure Your NLP Pipeline

1. Data audit — analyze your notes (volume, languages, structure).
2. Model selection — choose baseline (ClinicalBERT, GPT-4, Llama 3).
3. Fine-tuning — fine-tune on your labeled data (lr=2e-5, batch=16).
4. Integration — embed into your EHR via FHIR API.
5. Testing — evaluate F1, inference time, p99 latency.
6. Deployment — on-premise or cloud with monitoring.

Data Integration

HL7 FHIR API

The modern RESTful standard. We implement FHIR R4 servers: HAPI FHIR, medplum, Firely. We support SMART on FHIR for embedding AI apps into EMR via OAuth2.

Comparison: Manual vs AI Coding

Key Metrics of AI Modules

What’s Included in Development

• Audit of current documentation and data
• Design of NLP pipeline (NER, classification, summarization)
• Fine-tuning models on your data
• Integration via FHIR / SMART on FHIR
• Deployment (on-premise or cloud)
• Staff training
• Documentation and 3 months of support

Our Work Process

Analytics → Design → Implementation → Testing → Deployment → Support. Timeline: 3 to 6 months depending on data volume and integrations.

Contact us to get a consultation and preliminary assessment for your project.