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CASE STUDIES

Automatic analysis of medical documentation and patient data

Implementation:

2026

Industry:

MedTech / Healthcare IT / Digital Psychiatry (Digital Health)

Client's Business Profile:

A network of medical facilities and clinical hospitals specializing in psychiatric and psychological care, simultaneously conducting intensive research and development activities in the field of innovative diagnostic methods.

Implementation:
Industry:
Client's Business Profile:

2026

MedTech / Healthcare IT / Digital Psychiatry (Digital Health)

A network of medical facilities and clinical hospitals specializing in psychiatric and psychological care, simultaneously conducting intensive research and development activities in the field of innovative diagnostic methods.

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Challenges

Psychiatry, unlike other medical fields, relies almost entirely on textual data – interview descriptions, patient histories, and doctors’ notes. This specificity generated critical operational and compliance challenges:

  • Key information about the patient's health was scattered across many medical texts. Manual synthesis of this data for diagnostic purposes was time-consuming and prone to symptom omission.
  • Time-consuming medical coding (ICD-10): Manually assigning international disease classification codes (ICD-10) based on medical history descriptions required extensive, specialized knowledge and hundreds of hours of work from doctors and medical coders.
  • The implementation of advanced LLM systems in medicine (especially in the sensitive area of mental health) was associated with uncompromising requirements for personal data protection (GDPR) and the necessity to maintain the highest substantive quality of the system.

Solution

The answer was to design an intelligent natural language processing system, optimized for the analysis of Polish medical and psychiatric terminology, while maintaining full data sovereignty and security.

  • The system semantically processes unstructured texts, identifying key symptoms, linguistic patterns, and final clinical diagnoses.
  • A dedicated text classification model was implemented, which automatically suggests the most probable disease unit codes from the ICD-10 classification based on linguistic analysis of medical notes.
  • An environment meeting rigorous standards for processing sensitive data was built. This solution created a stable technological foundation for further advanced scientific research on supporting psychiatric diagnostics using artificial intelligence.

What the client gained

Proof

Advanced NLP models are capable of classifying complex diagnoses based on raw textual data.

Relief for staff from tedious work

Through the automation of initial medical documentation analysis and ICD-10 code prediction.

Ready-made platform and unique know-how

For building advanced CDSS (Clinical Decision Support Systems) tools. This paved the way for implementing systems that can assist psychiatrists in real-time, suggest alternative diagnostic pathways, and minimize the risk of medical errors.