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Clusters of nosocomial infection often occur undetected, at substantial cost to the medical system and individual patients. We evaluated binary cumulative sum (CUSUM) and moving average (MA) control charts for [...]

Byline: Caglar S. Aksezer, James C. Benneyan Originally developed in the late 1970s to assess the efficiency of comparable operating units, Data Envelopment Analysis (DEA) has since been used in a variety of contexts. Although incomplete data sets are often encountered in practice, the best approach in such situations is unclear in general. This paper explores methods such as multiple imputation, bootstrapping and smart dummy variable replacement, borrowed from similar missing data problems in regression analysis. Each missing data method is tested on a library of DEA problems that are gathered from the DEA literature. These problems are selected in such a way as to represent a thorough cross-section of problem sizes (small, medium, large) and types (type of DEA model, number of decision-making units, number of inputs, number of outputs, etc.). The results are illustrated by comparing the solutions of complete data sets against the simulated versions of the same data sets with missing data. The sensitivity of each method on the efficiency scores and ranking of the decision-making units is presented.

Byline: James C. Benneyan, Aysun Sunnetci, Mehmet Erkan Ceyhan We illustrate the use of Data Envelopment Analysis (DEA) models within process improvement work for identifying and benchmarking the best healthcare systems, in terms of most efficiently producing desirable outcomes from consumed resources. This approach is useful when comparing several systems that use multiple types of inputs (e.g., operating costs, clinicians, staff) to produce multiple outputs (e.g., outcomes, satisfaction, access), such as those commonly found in balanced scorecards and dashboard datasets, and provides the analyst with relative scores and rankings for each system, targets for each measure that would move inefficient systems to the best performance frontier, and a list of other systems to benchmark and emulate in order to improve. Modified DEA models are proposed to address four common issues that frequently arise in such contexts, including rationally constraining the weights given to each measure and handling missing, estimated or proportional data (such as adverse event or mortality rates). These models can be used to compare hospitals, departments, national healthcare systems, and regional or state systems and are useful to help understand how to improve sub-optimal processes and set feasible targets. This approach is illustrated at department, hospital, state, and country levels, with overall results showing very little correlation with less quantitative benchmarking studies.

Byline: James C. Benneyan Although the value of Statistical Process Control (SPC) charts to monitor and improve healthcare processes has received growing interest, several important implementation and theoretical issues are largely not understood. SPC charts are chronological graphs of process data that are used in many other industries to help control and improve quality and, while based in statistical theory, are easy for practitioners to use and interpret. This article provides an overview of the use of SPC in healthcare process improvement and Six Sigma activities, including the process of selecting and using the appropriate type of control chart, their statistical performance, and the guidelines for determining the appropriate sample sizes. The operating characteristics are computed and tabulated for a wide range of scenarios in order to aid practitioners in designing control charts with the desired statistical properties.