Abstract
BackgroundModelling is an important part of information science. Models are abstractions of reality. We use models in the following contexts: (1) to describe the data and information flows in clinical...
Highlights
Models allow abstraction of real-world phenomena based on our information needs.[1]
A large number of factors were identified that might impact on the diagnosis of asthma; these are illustrated on the first rich picture (Figure 3)
We developed a high-level data flow diagrams (DFDs) of how databases of routine health data might be used to identify the case of asthma (Figure 5) in the context of the English National Health Service (NHS)
Summary
Models allow abstraction of real-world phenomena based on our information needs.[1]. The nature of the abstracted reality largely depends on who builds the model and which technique is used. We use modelling to understand how clinical cases are recorded in record systems and to model health care business processes.[4,5,6]. We use models in the following contexts: (1) to describe the data and information flows in clinical practice to information scientists, (2) to compare health systems and care pathways, (3) to understand how clinical cases are recorded in record systems and (4) to model health care business models. Objective To demonstrate an approach for modelling complexity in health using asthma prevalence and incidence as an exemplar. Creating Unified Modelling Language (UML) use case diagrams to describe the interaction of the key actors with the system; 4. Genetic predisposition to atopy; exposure to environmental triggers; impact of respiratory
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