Designing a risk-adjusted CUSUM control chart based on DEA and NSGA-II approaches A case study in healthcare: Cardiovascular patients

Abstract. Researchers have recently devoted much attention to the development of control charts for monitoring healthcare systems. Accordingly, the purpose of this paper is to design a risk-adjusted cumulative sum (CUSUM) control chart to detect decreasing shifts. The proposed chart is used to monitor the survival times of patients who may be subject to an assignable cause such as human error during a surgery. To this end, risk adjustment is performed to consider the impact of each patient's preoperative risks on survival times using survival analysis regression models. However, using the risk-adjusted CUSUM requires that the chart parameters be determined. Hence, a multi-objective economic-statistical model is proposed and a two-stage solution method composed of Non-dominated Sorting Genetic Algorithm (NSGA-II) and Data Envelopment Analysis (DEA) is implemented to solve the model and obtain the optimal design parameters. The performance of the proposed approach is also studied in a real cardiac surgery center. Finally, to conrm the effectiveness of the proposed multi-objective design, two comparisons with the bi-objective and pure economic designs are made. The results show that the performance of the risk-adjusted CUSUM obtained from the proposed model is better than the two other designs in terms of statistical and economic properties.