We propose an approach for dynamic efficiency evaluation across multiple organizational dimensions using data envelopment analysis (DEA). The method generates both dimension-specific and aggregate efficiency scores, incorporates desirable and undesirable outputs, and is suitable for large-scale problem settings. Two regularized DEA models are introduced: a slack-based measure (SBM) and a linearized version of a nonlinear goal programming model (GP-SBM). While SBM estimates an aggregate efficiency score and then distributes it across dimensions, GP-SBM first estimates dimension-level efficiencies and then derives an aggregate score. Both models utilize a regularization parameter to enhance discriminatory power while also directly integrating both desirable and undesirable outputs. We demonstrate the computational efficiency and validity of our approach on multiple datasets and apply it to a case study of twelve hospitals in Ontario, Canada, evaluating three theoretically grounded dimensions of organizational effectiveness over a 24-month period from January 2018 to December 2019: technical efficiency, clinical efficiency, and patient experience. Our numerical results show that SBM and GP-SBM better capture correlations among input/output variables and outperform conventional benchmarking methods that separately evaluate dimensions before aggregation.

翻译：我们提出了一种跨多个组织维度进行动态效率评估的方法，该方法基于数据包络分析。该技术能够生成特定维度与综合效率得分，整合期望产出与非期望产出，并适用于大规模问题场景。我们引入两种正则化DEA模型：基于松弛测度的模型，以及非线性目标规划模型的线性化版本。前者先估计综合效率得分再将其分配至各维度，而后者则先估算维度级效率再推导综合得分。两种模型均利用正则化参数增强区分能力，并直接整合期望与非期望产出。我们通过多个数据集验证了方法的计算效率与有效性，并将其应用于加拿大安大略省十二家医院的案例研究，对2018年1月至2019年12月24个月期间的组织有效性三个理论维度（技术效率、临床效率与患者体验）进行评评估。数值结果表明，SBM与GP-SBM能更好地捕捉输入/输出变量间的相关性，并优于先分别评估各维度再进行集成的传统基准方法。