Abstract
Health organisations have adopted technologies since the 1960s, but only after the Industry 4.0 were such technologies systematized and organised under the H4.0 acronym. The pace at which digital information and communication applications have been developed in recent years challenge healthcare managers to choose assertively those with the largest potential impacts on their operations. In this paper, we propose using value stream mapping, a technique from the lean healthcare (LH) toolbox, to guide the choice of H4.0 digital applications that are more likely to support the improvement of value flows in healthcare organisations. We propose a three-step method, starting with mapping current and future value streams of the process under analysis, gathering data from team members on the indicated kaizen bursts and H4.0 digital applications, and finally assessing and ranking H4.0 digital applications that best support improvements and comply with attributes that characterise successful technological innovations. Our propositions are illustrated through a case study conducted in the sterilisation unit of a large public university hospital. Our findings indicate that three H4.0 digital applications should be prioritised to support the improvement of the value stream under analysis. Our method combines the simplicity of LH with more sophisticated solutions brought by H4.0.
Data availability statement
Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Notes on contributors
![](/cms/asset/5c36823b-0398-4668-a5ff-62cbe678fe1d/tprs_a_2048115_ilg0001.gif)
Guilherme Luz Tortorella
Guilherme Luz Tortorella is a Senior Lecturer at the University of Melbourne, Australia. He is the Head of Research of the Productivity and Continous Improvement Lab and the Editor-in-Chief of Journal of Lean Systems. He is one of the founders of the Brazilian Conference on Lean Systems and has more than 19 years with practical and academic experience with manufacturing and operations management.
![](/cms/asset/a94acb7e-cd74-4a6f-bd73-cc54eed795a6/tprs_a_2048115_ilg0002.gif)
Flavio Sanson Fogliatto
Flavio S. Fogliatto holds a Full Professor position in the IE Dept of the Federal University of Rio Grande do Sul, Brazil. He received his PhD in Industrial & Systems Engineering from Rutgers University, USA. Prof. Fogliatto specializes in the research areas of Quality Engineering, Operations Research, and Healthcare Analytics. His work has been published in Chemometrics, PP&C, Computers & Industrial Engineering, International Journal of Production Research and International Journal of Production Economics, among others.
![](/cms/asset/10740488-f2ec-4aa4-9338-e1e168c6fa4f/tprs_a_2048115_ilg0003.gif)
Diego Tlapa Mendoza
Diego Tlapa Mendoza is an Associate Professor of Industrial Engineering at the Universidad Autónoma de Baja California in Ensenada, Mexico. His research is mainly focused on lean systems and six sigma with implementation that varies from manufacturing to service industries.
![](/cms/asset/7ddbf11d-e7d7-4e65-bc1f-65da71b7fb55/tprs_a_2048115_ilg0004.gif)
Matthew Pepper
Matthew Pepper is an Associate Professor at the University of Wollongong, Australia, and has undertaken research and consultancy across a range of industry sectors, including the manufacturing and process industries as well as local government. Most recently his work has focused on the implementation of continuous improvement in service environments.
![](/cms/asset/ab5627e0-bb90-4dcc-bcc7-54ab97abe11f/tprs_a_2048115_ilg0005.gif)
Daniel Capurro
Daniel Capurro is a MD trained in Internal Medicine and holds a PhD in Biomedical and Health Informatics from the University of Washington in Seattle. He is the Deputy Director of the Centre for Digital Transformation of health where he co-leads the Digital health Validitron (a pipeline to validate digital health innovations in a way similar to what happens with drugs and vaccines) and the Data Science stream.