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

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. 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. 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. 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.

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© 2020 Alkaabi et al.

Déclaration de conflit d'intérêts

The authors declared no potential conflicts of interest with respect to the authorship and/or publication of this article. The part of background section of this paper was presented at the IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) as a poster presentation with interim findings. The poster’s paper was published in the proceeding of the 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM): doi: 10.1109/IEEM44572.2019.8978929.