Waste savings in patient transportation inside large hospitals using lean thinking tools and logistic solutions

Purpose - This paper deals with the issue of patient transportation inside large public hospitals and its costs. This cost is often related to transportation within departments, wards and outpatient clinics. The aim of this paper is to demonstrate through a qualitative case study how particular tools derived from Lean Thinking such as spaghetti chart, value stream mapping and activity worksheet can help to reduce costs related to patient transportation and other kinds of wastes. In particular the case study analyses the patient path in case of trauma with suspected fractures from the emergency department to patient discharge or hospitalisation. Design/methodology/approach - The paper is based on a qualitative case study. Although the case study has been carried out in a large Italian public hospital, the results can be generalised to all hospitals that are trying to reduce the cost of patient transportation and other kinds of wastes. Indeed, the Lean Thinking tools used here are suitable for all healthcare industries. Findings - The paper demonstrates how tools that were typically derived from the manufacturing sector can also be suitable for the healthcare sector. The results achieved have reduced the average lead time of the patient from the emergency department to hospitalisation or discharge. Considering that the patient paths are made with a stretcher or with a wheelchair pushed by a nurse, the reduced lead time leads to an interesting cost reduction. Practical implications - This paper is particularly helpful for healthcare practitioners. Managers and practitioners inside healthcare organisations could apply the same logistic solutions and the same tools to analyse the cost of many processes and specifically those in patient path and transportation. Originality/value - The paper addresses the literature gap regarding the use of specific tools such as spaghetti chart and activity worksheet in the large hospitals.