Imperial College London
Portrait Picture

Professor Mitch Blair

Faculty of Medicine, School of Public Health

Emeritus Professor
 
 
 
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Contact

 

+44 (0)20 8869 3881m.blair Website

 
 
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Location

 

River Island Academic Centre for Paediatrics and Child HealthNorthwick ParkNorthwick Park and St Marks Site

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Summary

 

Publications

Citation

BibTex format

@article{Liyanage:2016:10.14236/jhi.v23i1.863,
author = {Liyanage, H and Luzi, D and De, Lusignan S and Pecoraro, F and McNulty, R and Tamburis, O and Krause, P and Rigby, M and Blair, M},
doi = {10.14236/jhi.v23i1.863},
journal = {Journal of Innovation in Health Informatics},
pages = {476--484},
title = {Accessible Modelling of Complexity in Health (AMoCH) and associated data flows: asthma as an exemplar},
url = {http://dx.doi.org/10.14236/jhi.v23i1.863},
volume = {23},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Liyanage,H
AU  - Luzi,D
AU  - De,Lusignan S
AU  - Pecoraro,F
AU  - McNulty,R
AU  - Tamburis,O
AU  - Krause,P
AU  - Rigby,M
AU  - Blair,M
DO  - 10.14236/jhi.v23i1.863
EP  - 484
PY  - 2016///
SN  - 2058-4555
SP  - 476
TI  - Accessible Modelling of Complexity in Health (AMoCH) and associated data flows: asthma as an exemplar
T2  - Journal of Innovation in Health Informatics
UR  - http://dx.doi.org/10.14236/jhi.v23i1.863
UR  - http://hdl.handle.net/10044/1/48767
VL  - 23
ER  -
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