Optimizing Hospital Performance Evaluation: A Mixed-Methods Study to Develop a Multidimensional Framework for Scientific Validity, Structural Fairness, and Staff Motivation

Background Hospital performance evaluation is essential for ensuring accountability, improving service quality, and fostering staff engagement. Yet, existing systems often lack transparency, consistency, and motivational relevance—particularly for administrative and logistical staff whose contributions are frequently misaligned with standardized evaluation criteria. Methods This study employed a three-stage mixed-method design. In Stage One, a 21-item questionnaire was developed to assess three core dimensions: scientific validity, structural fairness, and staff motivation. Hierarchical cluster analysis was used to refine item groupings and validate structural coherence. In Stage Two, 142 administrative and logistical staff completed open-ended surveys. Responses were thematically coded using an artificial intelligence language model to extract high-frequency concepts across dimensions. In Stage Three, a 15-item structured voting questionnaire was created based on the coded themes. Participants selected preferred reform strategies for each domain. Results Cluster analysis confirmed three balanced item clusters, supporting the construct validity of the questionnaire. Thematic analysis revealed five recurring concerns: absence of role-specific indicators, weak feedback mechanisms, inadequate incentives, misalignment between evaluation and job responsibilities, and limited staff participation. In the voting survey, participants showed strong consensus on targeted reforms. For scientific validity, 72% favored linking performance tiers to benefits; 74% supported indicators for innovation and cross-department collaboration. For structural fairness, 74% preferred tenure-adjusted scoring models; 79% endorsed simplified policy communication. For motivation, 81% supported role-specific performance models and contribution-based incentives. The majority of participants endorsed the proposed reform strategies in 14 of the 15 items. Conclusion This study presents a stakeholder-informed, replicable model for optimizing hospital performance evaluation. Through a combination of statistical analysis, artificial intelligence-supported thematic extraction, and structured stakeholder input, we identified actionable gaps and staff-driven solutions. Recommended improvements include developing role-specific key performance indicators, implementing transparent feedback and appeal systems, introducing differentiated incentives, and integrating education and tenure into scoring logic. Institutional transparency and staff participation emerged as critical factors for enhancing credibility and engagement. The proposed multidimensional framework offers practical guidance for improving scientific rigor, structural fairness, and motivational alignment in hospital performance systems.