Medical Informatics
Research Article
Internal quality control system for medical care using artificial intelligence
Aleksey Aleksandrovich Belchenkov1, Vladimir Viktorovich Kalinovsky2, Dmitriy Vladimirovich Belyshev3
, Anton Sergeevich Klochkov4
| 1,2 | Interin Group of Companies, Moscow, Russia |
| 3 | Ailamazyan Program Systems Institute of RAS, Ves'kovo, Russia |
| 4 | Rehabilitation Center „Three Sisters“, Moscow, Russia |
| 3 |
belyshev@interin.ru |
Abstract. This article presents the experience of developing and implementing an automated quality control system for medical care, "Case History Supervision," in a rehabilitation clinic. The solution combines classic algorithms for processing structured data from a medical information system (MIS) with the analysis of unstructured text from electronic medical records using large language models (LLM).
Objective: To develop and test a methodology for automated quality control of medical care that ensures complete coverage of patient case histories with minimal expert involvement during data collection and initial analysis.
Materials and Methods: The study was conducted using the Interin PROMIS Alpha PG medical information system at the Three Sisters Early Rehabilitation Clinic. The methodology includes two types of evaluation criteria: algorithmic (calculated using DBMS tools based on structured data) and intelligent (evaluated using LLM based on the text content of medical documents).
Results. A "Case History Supervision" software module has been developed and implemented, enabling the automatic generation of a comprehensive assessment of the quality of case history management based on a configurable set of criteria. The module implements a hybrid approach to data analysis and is integrated into the clinic's workflows.
Conclusions. The combination of algorithmic methods and artificial intelligence technologies enables effective scalable and comprehensive quality control of medical documentation. The system does not replace experts, but frees up their time for analyzing complex cases, serving as a decision support tool. Future developments include the transition to continuous monitoring and the use of specialized medical LLMs. (In Russian).
Keywords: artificial intelligence in medicine, large language models, LLM, quality control of medical documentation, medical information systems, healthcare automation
MSC-2020
94A05; 92C50, 93Bxx
For citation: Aleksey A. Belchenkov, Vladimir V. Kalinovsky, Dmitriy V. Belyshev, Anton S. Klochkov. Internal quality control system for medical care using artificial intelligence. Program Systems: Theory and Applications, 2025, 16:6, pp. 221–235. (In Russ.). https://psta.psiras.ru/2025/6_221-235.
Full text of article (PDF): https://psta.psiras.ru/read/psta2025_6_221-235.pdf.
The article was submitted 03.11.2025; approved after reviewing 04.11.2025; accepted for publication 17.11.2025; published online 15.12.2025.
