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A sequential model for the structure of health care utilization

Traditional measurement models of health care utilization are not able to represent the complex structure of health care utilization. In this qualitative study, we, therefore, developed a new model to represent the health care utilization structure. In Norway and Germany, we conducted episodic inter...

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Detalles Bibliográficos
Autores principales: Herrmann, Wolfram J., Haarmann, Alexander, Bærheim, Anders
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5428914/
https://www.ncbi.nlm.nih.gov/pubmed/28498872
http://dx.doi.org/10.1371/journal.pone.0176657
Descripción
Sumario:Traditional measurement models of health care utilization are not able to represent the complex structure of health care utilization. In this qualitative study, we, therefore, developed a new model to represent the health care utilization structure. In Norway and Germany, we conducted episodic interviews, participant observation and a concurrent context analysis. Data was analyzed by thematic coding in the framework of grounded theory. Consultations do very often not only have one single reason for encounter. They are usually not independent events but form part of consultation sequences. We could find structural differences between Norway and Germany regarding the flow of information between consultations and which providers are involved in health care in what way. This leads to a sequential model, in which health care utilization is seen as sequences of consultations. Such health care utilization sequences consist of nodes which are connected by edges. Nodes represent patient-provider contacts and edges depict the flow of information. Time and the level of health care providers are dimensions in the model. These sequences can be described by different measures and aggregated on population level. Thus, the sequential model can be further used in analyzing health care utilization quantitatively, e.g., by using routine data.