Executive summary Data envelopment analysis (DEA) is an approach used in the examination of multiple input and multiple output processes. DEA necessitates neither a clear formulation of the fundamental practical correlation nor pre-allotted weights for multi-outputs and multi- inputs in assessing performance concerning a process (Chan, Johansen, Mangolini, & Peacock, 2001). The key benefit of using this approach is its capability to unambiguously factor in the use of numerous inputs to designate numerous outputs. Similarly, DEA aids in limiting the intricacy of examination through the simultaneous assessment of the qualities of interest and offering a solitary and compound tally denoted as efficiency. Problem definition American health care spending is higher compared to the spending of other developed countries (Sherman & Zhu, 2013). In the year 2011, $2.7 trillion was spent. The above implies that approximately $8, 680 were spent per person. As compared to the spending used in the years 2009 and 2010, the amount spent in the year 2011 grew by 3.9% to $ 850 billion. The US spends over 20% of its annual budget on health care. In regard to this, is apparent that cost containment in the health care sector should be implemented. While implementing the needed changes, the sustained capability of hospitals to provide effective and standard service should be upheld. Of particular importance to health care executives is the capability to benchmark features of their organizations’ productivity concerning their rivals. During benchmarking, several challenges are encountered. The challenges are how to develop means of operating efficacy for hospitals, how to select weights to be used in several inputs and outputs, how to undertake to benchmark, and how verdicts like the termination of some divisions should be carried out (Jacobs, 2000). The above challenges can be addressed by adopting DEA. Solution detail For a hospital manager to analyze the efficacy of a specific DMU carefully worked-out programming procedures are used to define weights for the comparative value of the numerous outputs and inputs that make the most of an exact DMU’s efficiency score (Sherman & Zhu, 2006). The most predominant DEA model designs in the market are attributed to Charnes, Cooper, and Rhodes. The model is illustrated below:        For each                       DMU p              P = 1, 2,….,                                                                                E p =              ∑ y pi w i                                      Maximize                                          i                                   (1)                                          ∑ x pj v j                                                                                                                                                                                                                                                                j                                     Subject to:                                                                                                                                                              ∑ y ki w i                                                                                                      i                               1  for  k = 1,2,…., K  (2)