An Efficiency-Based Multicriteria Strategic Planning Model for Ambulatory Surgery Centers

Ambulatory surgery centers (ASCs) provide a low-cost alternative to traditional inpatient care. In addition, with health care reform imminent, it is likely that many currently uninsured people will soon acquire health care coverage, significantly increasing the demand for health services. ASCs are among the providers that can expect to see a substantial amount of this new pent-up demand and, therefore, ASCs are likely to continue their current growth into the foreseeable future. Those ASCs that plan accordingly by optimizing procedure mix and volume will benefit most from the increased demand. We propose a two-stage efficiency-based multicriteria decision model to guide an ASC in identifying its optimal procedure mix. The first stage uses Data Envelopment Analysis (DEA) to calculate the efficiency of each procedure based on the resources required to perform the procedure, the revenue it generates, and its risk of complications. The second stage uses the DEA factor efficiency scores in a bottleneck program to optimize the mix of procedures while satisfying the ASC’s resource and operational constraints. The criteria are to (1) maximize reimbursement while (2) minimizing the total number of complications. We demonstrate the approach using a data set based in part on data from an actual ASC.

Authors and Affiliations

1. Stony Brook University, Stony Brook, NY, USA

   Herbert F. Lewis, Thomas R. Sexton & Melissa A. Dolan

Corresponding author

Correspondence to Thomas R. Sexton.

Appendix: Data Envelopment Analysis

Data Envelopment Analysis (DEA) has become a widely used methodology for evaluating relative efficiency. We trace its mathematical development to Charnes et al. [15], who built on the work of Farrell [16] and others. DEA measures relative efficiency in situations in which there are multiple inputs and outputs and there is no obvious objective way to aggregate either inputs or outputs into a meaningful index of productive efficiency. The technique is well documented in the management science literature [15, 17–22], and it has received increasing attention as researchers have wrestled with problems of productivity measurement, especially in the services and nonmarket sectors of the economy. Anderson [7] and Emrouznejad [8] have each provided a web site with extensive bibliographies of over 1,000 articles that document the theoretical development of DEA and its broad range of application. Tavares [23] provides a comprehensive bibliography of the DEA literature. In addition, many management science texts cover DEA. Anderson et al. [24] and Winston and Albright [25] present a good introduction to this topic.

In its basic form, DEA considers a collection of decision-making units (DMUs) each of which consumes DMU-specific levels of selected inputs to produce DMU-specific levels of selected outputs. DEA makes no assumptions regarding the manner in which a DMU converts inputs into outputs; each DMU is a “black box” with respect to its production process. DEA models allow for differing assumptions regarding returns-to-scale. In addition, DEA models may be input-oriented, output-oriented, or un-oriented. Input-oriented models identify input reductions that would enable a DMU to become efficient while output-oriented models identify output increases that would achieve the same effect. Un-oriented models identify a mix of input reductions and output increases that lead to efficiency.

DEA establishes an efficient frontier based on observed best performances and evaluates the efficiency of each DMU relative to this frontier. DMUs that lie on the frontier are efficient. DEA evaluates the efficiency of a DMU that does not lie on the frontier relative to a linear combination of the efficient DMUs. This linear combination represents an empirically feasible reference DMU that dominates the inefficient DMU under evaluation. The reference DMU consumes no more of each input while producing at least as much of each output as does the DMU under evaluation. The DEA model finds the most productive reference DMU and computes the efficiency of the DMU under evaluation relative to this reference DMU. For example, if the reference DMU produces at least 25% more of every output while consuming no more of each input, then the inverse efficiency of the DMU under evaluation is 1.25 and its efficiency is 1/1.25 = 0.8. We can formulate the DEA model for a specific DMU as a mathematical program. A complete DEA requires that we solve one such mathematical program for each DMU.

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