Document Type : Original Article
Authors
1
Department of Health Economics and Health Services Research, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
2
Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
3
Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
4
Erasmus Centre for Health Economics Rotterdam (EsCHER), Rotterdam, The Netherlands
5
Institute for Advanced Study, University of Amsterdam, Amsterdam, The Netherlands
6
Independent Complex Systems Researcher, Noordwijk, The Netherlands
7
Department of Epidemiology and Data Science, Amsterdam University Medical Centre, Vrije Universiteit, Amsterdam, The Netherlands
8
Department of Psychology, Faculty of Social and Behavioral Sciences, University of Amsterdam, Amsterdam, The Netherlands
9
Center for Prevention, Lifestyle and Health, Department Behaviour & Health, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
Abstract
Background
Approximately 20% of the total healthcare expenditure in high-income countries is spent on low-value care, ie, care that is unnecessary, potentially harmful, or provides marginal health benefits to patients. Demand for lowvalue care is considered a multifactorial, complex problem, as a multitude of factors have been associated with low-value care use. However, there is limited knowledge on how these factors interrelate and lead to demand for low-value care. Therefore, the aim of this study was to explore and map the factors and their relations contributing to patients’ demand for low-value care using a complex systems approach.
Methods
Two group model building (GMB) sessions on the topic of low-value care were organised with experts from the Netherlands. Each session’s transcript was thematically analysed, resulting in one causal loop diagram (CLD) per session. These CLDs included determinants, relations between these determinants and feedback loops. Finally, the CLDs were synthesised into one CLD combining the insights from both sessions.
Results
The final CLD consisted of 42 factors influencing demand for low-value care. It includes biomedical factors, cognitive biases, socio-cultural factors, economic factors, emotions, knowledge-related factors, factors related to the interaction with the provider, and preferences and expectations. By mapping the relations between these factors, we identified 59 connections and nine reinforcing feedback loops potentially influencing demand for low-value care.
Conclusion
The CLD provides insight into factors, mechanisms and feedback loops influencing patients’ demand for low-value care. It highlights perceived insecurity as a central driver that influences multiple other factors and eventually affects patients’ demand for low-value care. These central factors influencing multiple other factors may be potential leverage points for policies aiming to reduce demand for low-value care. Further research is required to clarify the relative importance of the identified factors, relationships, and feedback loops to determine effective leverage points.
Keywords