Objective: To analyze demographic traits, clinical complications, and healthcare use in patients with chronic liver disease across major etiologies in a large Romanian cohort. Methods: A retrospective study (2019–2023) of 2359 patients with chronic hepatitis C (CHC), hepatitis associated with alcohol (ALH), cirrhosis associated with alcohol (ALC), or non-alcoholic cirrhosis (NALC). Data on demographics, clinical outcomes, and hospitalizations were analyzed using descriptive statistics, regression modeling, and clustering in IBM SPSS 27.0.1. Results: CHC patients were oldest (mean 67.5 ± 12.3 years), while ALH patients were youngest (56.0 ± 11.0 years). CHC prevalence increased with age (10.0% in ≤30-year-olds to 87.1% in ≥81-year-olds; γ = 0.535, p < 0.001). Females comprised 60–70% of CHC cases, males > 85% of ALH and >78% of ALC. Mean hospitalization duration decreased from 13.80 days (2019) to 9.10 days (2023), yet cirrhotic patients had the longest stays (NALC: 16.37 ± 14.34; ALC: 17.66 ± 12.96) versus CHC (10.38 ± 10.14). Etiology was the strongest predictor of hospitalization length. Portal hypertension (PH) was the most common complication (54.3%), with males bearing more severe hepatic complications (ascites—38.3%; PH—66.8%). Conclusions: Hospital-based Romanian cohort analysis revealed that patient presentation and outcomes are fundamentally shaped by the interplay of etiology, sex, and age. We found a distinct female predominance in CHC, a pronounced male predominance in alcohol-related diseases, and evolving trends in non-alcoholic cirrhosis. These determinants dictate specific epidemiological patterns, hospitalization burdens, and complication risks, underscoring the critical need for a paradigm shift toward personalized, etiology-driven, and sex-tailored clinical management.

Sustainable manufacturing performance is increasingly influenced by the quality of decisions embedded in Quality Management System (QMS) activities, particularly those related to problem analysis and preventive action. In industrial environments such as welded bus chassis production, recurring quality defects—although involving small components—can generate sustainability impacts through rework, inspection effort, and energy consumption. Although artificial intelligence (AI) is increasingly adopted to support quality-related tasks, its contribution is often assessed in terms of automation rather than its effect on decision quality. This study presents an AI-supported, prompt-driven decision framework designed to strengthen preventive performance within QMS. The framework is implemented through a deterministic software application that formalizes prompt engineering as a rule-based process, transforming informal human problem descriptions into structured prompts suitable for external AI reasoning tools. The application itself does not embed AI and does not generate decisions; instead, it functions as a transparent decision interface that reduces variability in problem formulation and supports methodological consistency. The framework was validated through an industrial case study conducted in a bus chassis manufacturing plant experiencing recurring defects related to missing or incorrectly positioned welded brackets. Quantitative evaluation using Key Performance Indicators demonstrates reduced analysis cycle time, improved completeness of problem definitions, higher corrective action implementation rates, and lower defect recurrence.