Acute viral intestinal infections (AII) remain one of the most significant problems in the structure of infectious morbidity in the Russian Federation. The aim of the study: To identify the impact of the quality of inpatient medical care on the level of treatment adherence in patients with acute viral intestinal infections and to develop proposals for optimizing the payment mechanism under the Clinical Statistical Group (CSG) to improve the efficiency of care in an infectious diseases hospital in the Sverdlovsk Region. Materials and methods. A retrospective cohort study (n=678 medical records) and a prospective survey (n=180 patients) were conducted in the Infectious Diseases Department of City Hospital for 2024–2025. Adherence was assessed using the Morisky-Green scale (MMAS-4). Statistical analysis included descriptive statistics, Mann–Whitney test, χ² test, Spearman correlation, and binary logistic regression. A clinical and economic analysis was performed. Results. The proportion of interrupted cases was 18.0% (122/678). The mean adherence score was 3.41±0.72. A strong negative correlation was found between hospitalization duration and adherence (r = –0.682; p<0.0001). Independent predictors of treatment interruption were stay ≥4 days (OR=3.87; 95% CI 2.14–7.02), age 18–39 years (OR=2.64), and low ward comfort rating (OR=1.92). The existing tariff model leads to chronic underfunding (49.9–55.3% of the plan) and creates a vicious circle. Conclusion. Optimization of tariff policy by introducing subgroup st12.008-1 with full payment for stays ≤3 days and an increasing coefficient of 1.08–1.12 will improve patient adherence, reduce interrupted cases by 13–20 percentage points, increase bed turnover by 28–35%, and provide additional funding of 4.1–5.4 million rubles per year for one department. The data substantiate the need to align tariff regulation with the clinical guidelines of the Ministry of Health of the Russian Federation.
Keywords: acute intestinal infections, viral gastroenteritis, patient adherence, Morisky- Green, quality of medical care, clinical statistical groups, tariff policy, short hospitalizations.