Quality Validation and Assessment Methodology

Wizey is a specialized tool for automating medical information processing. The system transforms unstructured data (anamnesis, test results) into standardized clinical documents.

• Clinical Interpretation: two-level analysis of laboratory data ("Doctor" profile / "Patient" profile).
• Personalized Communication: generation of clear memos and routing sheets for patients.
• CDSS (Clinical Decision Support System): formation of diagnostic hypotheses and examination plans (Second Opinion).
• Standardization: unification of medical documentation in accordance with clinic regulations.
• Seamless Integration: API-first approach for embedding into existing HIS/LIS or your internal processes.

More about use cases: /b2b/use-cases/. Technical documentation: /b2b/api/.

• Operational Efficiency: instant processing of large data arrays, reducing load on staff.
• Quality Control: ensuring unified standards of medical care and communication in all branches.
• Customization: adaptation of algorithms and generation templates to internal clinic protocols.
• Reliable Architecture: fault-tolerant asynchronous API with request idempotency support.
• Information Security: compliance with regulatory requirements, support for Zero-Log mode (no data saving).
• Results Transparency: we provide access to the partner's personal account so you can build clear efficiency metrics (KPI) for the pilot project.

Security Policy: /b2b/security/.

During internal testing on materials of the state final certification in the specialty "General Medicine", the system demonstrated a result of 92% correct answers.

This metric confirms the high competence of the algorithm in analyzing medical information, but is not a basis for autonomous clinical decision-making. In the B2B segment, the key indicator is the system's ability to stably and correctly structure data, minimizing the risk of diagnostic errors.

"Human Responsibility" Principle: the system functions in assistant mode, the final decision is always made by the doctor.

Testing was conducted based on the official collection of tasks for the final state certification in the specialty 31.05.01 "General Medicine".

Test Sample Structure (675 questions):

• Fundamental Disciplines (Anatomy, Biology, Biochemistry) — 210 questions
• Therapy (Pulmonology, Gastroenterology, Cardiology, Rheumatology, Hematology, Nephrology, Endocrinology) — 370 questions
• Surgery (Hospital Surgery) — 95 questions

Experiment Conditions: "blind" testing (the algorithm was not trained on this dataset).

Evaluation Criteria:

A strict metric (Exact Match) was used: the answer was counted as correct only if it fully matched the reference (including multiple-choice questions). Partially correct answers were evaluated as incorrect.

Result: 620 correct answers out of 675 (92%).

• System Status: CDSS (Clinical Decision Support System). Not a medical device for automatic diagnosis.
• Liability: the service provides analytical information. Responsibility for data verification and clinical decision-making lies with the medical specialist.
• Data Quality: the accuracy of the algorithm directly depends on the completeness and correctness of the input data (anamnesis, test results).
• Technical Requirements: the main data exchange format is text. Processing graphic files requires preliminary OCR.
• Data Storage: when using Zero-Log mode, responsibility for long-term storage of results lies on the client side.

Technical Documentation: /b2b/api/. Security Policy: /b2b/security/.

We offer to validate the system's quality on your data as part of a pilot project. We will agree on success criteria, configure generation templates, and ensure a secure integration perimeter.

Discuss Pilot Cooperation Terms