Process Optimization by Means of a Computerized Process Simulation Model in Cardiac Surgery

Abstract

Background

The increasing financial pressure to which hospitals are exposed as a result of changes in the healthcare system calls for detailed knowledge of the cost and revenue situation in the clinical environment. The establishment of structured cost-unit accounting has become an essential part of strategic control. On this basis, it is possible to identify cost-saving potentials and efficiency measures, e.g. by means of process optimization. This article discusses the development of computerized process simulation and its implementation within the context of a clinical pathway during the inpatient stay for elective coronary artery bypass grafting (CABG), in an attempt to optimize hospital processes. For the purposes of this study, the subprocess of ‘surgical operation’ for elective CABG (beginning with anesthesia preparation and ending with transfer to the intensive care unit) was simulated in order to identify parts of the process that could be modified to optimize the overall time of the process. For this purpose, two parts of the operation process were chosen as potential time-saving areas: (i) elimination of the time spent whilst a patient waits for transfer to the operating room; and (ii) elimination of the time spent preparing the operating table, by performing this task in parallel with anesthesia induction.

Methods

Simulations were performed using Corel iGrafix® Process™ 2003 software. Three scenarios were simulated: (i) the status quo (the current established sequence involved in the operation section of the clinical pathway); (ii) the sequence after elimination of the wait for transfer to the operating room; and (iii) the sequence after changing the preparation of the operating table so that it is performed in parallel to anesthesia induction rather than waiting until the patient is in the operating room.

Results

The results of 1000 simulation runs in each case indicated a significant reduction in the total patient throughput time, both in the elimination of time spent waiting for transfer to the operating room and in parallel process organization. In contrast with the status quo (triangular distribution), the total time for the treatment stage could be described by way of approximation with a normal distribution and a significant accumulation of minimum total times.

Conclusion

The results of this investigation demonstrated that the computerized simulation of treatment processes can make a valuable contribution to process optimization in the hospital. One particular advantage of the simulation module is that potential improvements that may result from economic and organizational changes can be predicted and tested before any practical implementation involving expense occurs. Naturally, only practical application will show whether the simulation results of the model can in fact be implemented in this way. On the other hand, with the aid of the model, any impracticable or uneconomic changes can be detected and avoided at an early stage, thus saving resources.