Accessible Modelling of Complexity in Health and associated data flows: Asthma as an exemplar

Abstract

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?ows 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 diffculties 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?ow diagrams (DFDs); 3. Creating Unifed 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?agged 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 fnding. Conclusions This approach?agged the factors that might impact on the reported prevalence or incidence of asthma. The models suggested that applying selection criteria may improve the specifcity of new or confrmed diagnosis.