University of Surrey

Test tubes in the lab Research in the ATI Dance Research

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

Liyanage, HS, Luzi, D, de Lusignan, S, Pecoraro, F, McNulty, R, Tamburis, O, Krause, P, Rigby, M and Blair, M (2016) Accessible Modelling of Complexity in Health and associated data flows: asthma as an exemplar Journal of Innovation in Health Informatics, 23 (1). pp. 476-484.

[img]
Preview
Text
Accessible Modelling of Complexity in Health.pdf - Version of Record
Available under License Creative Commons Attribution.

Download (2MB) | Preview
[img]
Preview
Text (licence)
SRI_deposit_agreement.pdf
Available under License : See the attached licence file.

Download (33kB) | Preview

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 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.

Item Type: Article
Subjects : Biosciences
Divisions : Faculty of Health and Medical Sciences > School of Biosciences and Medicine
Authors :
NameEmailORCID
Liyanage, HSUNSPECIFIEDUNSPECIFIED
Luzi, DUNSPECIFIEDUNSPECIFIED
de Lusignan, SUNSPECIFIEDUNSPECIFIED
Pecoraro, FUNSPECIFIEDUNSPECIFIED
McNulty, RUNSPECIFIEDUNSPECIFIED
Tamburis, OUNSPECIFIEDUNSPECIFIED
Krause, PUNSPECIFIEDUNSPECIFIED
Rigby, MUNSPECIFIEDUNSPECIFIED
Blair, MUNSPECIFIEDUNSPECIFIED
Date : 2016
Identification Number : 10.14236/jhi.v23i1.863
Copyright Disclaimer : Copyright © 2016 The Author(s). Published by BCS, The Chartered Institute for IT under Creative Commons license http://creativecommons.org/ licenses/by/4.0/
Uncontrolled Keywords : Health information exchange, Interdisciplinary Communication, Information systems, Informatics, Systems Analysis
Depositing User : Symplectic Elements
Date Deposited : 02 May 2017 10:06
Last Modified : 02 May 2017 10:06
URI: http://epubs.surrey.ac.uk/id/eprint/814072

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800