Modeling help chains in health services as social networks: moving from linearity to complexity

Abstract

Help Chain (HC) is a problem-solving practice underpinned by lean manufacturing principles and also adopted for health services, which have different complexity characteristics in relation to manufacturing. This study is based on the premise that Social Network Analysis (SNA) might be an effective analytical approach to account for the complexity of HCs in healthcare. Hence, two research questions are addressed: how to model and interpret HCs in health services as a social network and how SNA can either challenge or add to the lean assumptions of HC design. The use of SNA to model HCs was tested in a maternity hospital, where HCs related to five problems were analyzed, concerning the supply of instruments from the centre of sterilised materials to the surgical and obstetrics units. We analyzed the HCs of single problems and then combined the five problem-solving networks to produce a multilayered network that accounted for their interactions. The patterns of social interactions varied according to the problem and three dimensions of actors’ performance (i.e. availability, reliability, and agility). SNA unveiled the complexity of HCs and provided guidance for revising the lean assumptions in their design, matching the realities of health services.

KEYWORDS:

• Help chain
• social network analysis
• lean production
• complexity
• health services

Data availability statement

The participants of this study did not give written consent for their data to be shared publicly, so due to the sensitive nature of the research supporting data is not available.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The acronym HC is used throughout the paper solely to denote Help Chain. It is not to be confounded with healthcare, which will always be written in full.

Additional information

Notes on contributors

Bruno Miranda dos Santos

Bruno Miranda dos Santos holds a M.S. in Industrial Engineering from Federal University of Santa Maria (Brazil). He is a Ph.D. student at Federal University of Rio Grande do Sul. He is also a Data Developer Engineer focusing on digital transformation and technology for business. His research interests include lean healthcare, social networks, and data science application in business areas such as healthcare and industrial engineering.

Flavio Sanson Fogliatto

Prof. Flavio S. Fogliatto holds a Ph.D. in Industrial and Systems Engineering from Rutgers University, USA (1997). He was visiting scholar at the CNAM (Paris, France) in 2005–2006 and at the University of Calgary (Canada) in 2022–2023. Currently he is Full Professor at the IE Department of the Federal University of Rio Grande do Sul (Brazil). He is Associate Editor of IJO&PM. His research interests are healthcare operations management, quality control and optimisation of products and processes, mass customisation, and quantitative methods in production control.

Tarcisio Abreu Saurin

Tarcisio Abreu Saurin is a Full Professor at the Industrial Engineering Department of the Federal University of Rio Grande do Sul, Brazil. He holds a PhD in Industrial Engineering, a MS in Construction Management, and a BS in Civil Engineering. He was a visiting professor at the Australian Institute of Health Innovation at Macquarie University, and at the University of Salford, UK. His main research interests are related to safety management, lean production, resilience engineering, and healthcare management. On these topics, he has carried out research and consulting projects in healthcare, construction, electricity distribution, and manufacturing.

Guilherme Luz Tortorella

Guilherme Luz Tortorella is Associate Professor of the Department of Systems and Production Engineering of the Universidade Federal de Santa Catarina, Brazil. He is the Head of Research of the Productivity and Continuous 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 18 years with practical and academic experience with manufacturing and operations management.