Background Modelling 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 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.Asthma is an important condition associated with a substantial mortality and morbidity. However, there are difficulties in determining who has the condition, making both its incidence and prevalence uncertain.Objective To demonstrate an approach for modelling complexity in health using asthma prevalence and incidence as an exemplar.Method The four steps in our process are:1. Drawing a rich picture, following Checkland's soft systems methodology;2. Constructing data flow diagrams (DFDs);3. Creating Unified Modelling Language (UML) use case diagrams to describe the interaction of the key actors with the system;4. Activity diagrams, either UML activity diagram or business process modelling notation diagram.Results Our rich picture flagged the complexity of factors that might impact on asthma diagnosis. There was consensus that the principle issue was that there were undiagnosed and misdiagnosed cases as well as correctly diagnosed. Genetic predisposition to atopy; exposure to environmental triggers; impact of respiratory health on earnings or ability to attend education or participate in sport, charities, pressure groups and the pharmaceutical industry all increased the likelihood of a diagnosis of asthma. Stigma and some factors within the health system diminished the likelihood of a diagnosis. The DFDs and other elements focused on better case finding.Conclusions This approach flagged the factors that might impact on the reported prevalence or incidence of asthma. The models suggested that applying selection criteria may improve the specificity of new or confirmed diagnosis.
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 July 2018.
All research outputs
#23,300,432
of 34,365,356 outputs
Outputs from BMJ Health & Care Informatics
#345
of 499 outputs
Outputs of similar age
#209,155
of 338,578 outputs
Outputs of similar age from BMJ Health & Care Informatics
#4
of 8 outputs
Altmetric has tracked 34,365,356 research outputs across all sources so far. This one is in the 30th percentile – i.e., 30% of other outputs scored the same or lower than it.
So far Altmetric has tracked 499 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.1. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
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