Evaluation of System Modelling Techniques for Waste Identification in Lean Healthcare Applications.

PURPOSE: Waste identification plays a vital role in lean healthcare applications. While the value stream map (VSM) is among the most commonly used tools for waste identification, it may be limited to visualize the behaviour of dynamic and complex healthcare systems. To address this limitation, system modelling techniques (SMTs) can be used to provide a comprehensive picture of various system-wide wastes. However, there is a lack of evidence in the current literature about the potential contribution of SMTs for waste identification in healthcare processes. METHODS: This study evaluates the usability and utility of six types of SMTs along with the VSM. For the evaluation, interview-based questionnaires were conducted with twelve stakeholders from the outpatient clinic at the Heart and Vascular Institute at Cleveland Clinic Abu Dhabi. RESULTS: VSM was found to be the most useful diagram in waste identification in general. However, some SMTs that represent the system behaviour outperformed the VSM in identifying particular waste types, e.g., communication diagram in identifying over-processing waste and flow diagram in identifying transportation waste. CONCLUSION: As behavioural SMTs and VSM have unique strengths in identifying particular waste types, the use of multiple diagrams is recommended for a comprehensive waste identification in lean. However, limited resources and time, as well as limited experience of stakeholders with SMTs, may still present obstacles for their potential contribution in lean healthcare applications.

Toward Implementing Patient Flow in a Cancer Treatment Center to Reduce Patient Waiting Time and Improve Efficiency.

Outpatient cancer treatment centers can be considered as complex systems in which several types of medical professionals and administrative staff must coordinate their work to achieve the overall goals of providing quality patient care within budgetary constraints. In this article, we use analytical methods that have been successfully employed for other complex systems to show how a clinic can simultaneously reduce patient waiting times and non-value added staff work in a process that has a series of steps, more than one of which involves a scarce resource. The article describes the system model and the key elements in the operation that lead to staff rework and patient queuing. We propose solutions to the problems and provide a framework to evaluate clinic performance. At the time of this report, the proposals are in the process of implementation at a cancer treatment clinic in a major metropolitan hospital in Montreal, Canada.