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：本文提出一种利用数据包络分析（DEA）进行多组织维度动态效率评估的方法。该方法既能生成维度特异性效率评分，又能计算综合效率评分，同时整合了期望产出与非期望产出，适用于大规模问题场景。我们引入了两种正则化DEA模型：基于松弛变量的测度模型（SBM）以及非线性目标规划模型（GP-SBM）的线性化版本。SBM先估计综合效率分数再将其分配至各维度，而GP-SBM则先估计维度层面效率再推导综合分数。两种模型均通过正则化参数增强区分能力，并直接整合期望与非期望产出。我们在多个数据集上验证了该方法计算效率与有效性，并将其应用于加拿大安大略省12家医院的案例研究，评估了2018年1月至2019年12月共24个月期间组织效能的三个理论维度：技术效率、临床效率与患者体验。数值结果表明，相较于传统先分维度评估再聚合的基准方法，SBM与GP-SBM能更好地捕捉输入/输出变量间的相关性并表现出更优性能。