Six Sigma with Optimization and Probability Models in Healthcare Management

A population of rapid growth, a place of the booming economy, whistle-stop urbanization along with disease escalation requires persistent improvement in healthcare services. However, delays, medical errors, measurement errors and variability weaken the coherence of healthcare, which increases the pressure in redesigning the process for quality improvement. In this paper, Six sigma is implemented for selecting the parameters in health care management. Then optimization based on nonlinear programming is performed for minimizing the total cost in bed scheduling, where the constraints depend on the budget spent, types of doctors, the technicians, the medical equipment, etc. It also aims to minimize the total cost associated with packaging medications and emergency department allocation. Optimized results showed that the emergency department has to be taken care in order to gain a superlative performance of healthcare maintenance. Hence the usage of probability models based on Poisson regression has been imposed on bed occupancy of the emergency department by considering the number of arrivals and the number of departures of patients on specific days. The results are then examined using DMAIC techniques and are found to be under control using an exponentially weighted moving average chart. The computational behavior, the efficiency of the proposed model and the practical applicability is exemplified by a case study. This novel methodology of integrating six sigma with optimization techniques and probability models has gained a strong potential success related to cost reduction, increase in patient satisfaction and enables continuous monitoring of health care service, thereby promoting economic sustainability.