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

Harshana Liyanage, Daniela Luzi, Simon de Lusignan, Fabrizio Pecoraro, Richard McNulty, Oscar Tamburis, Paul Krause, Michael Rigby, Mitch Blair

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.


Keywords


Interdisciplinary Communication, information systems, health information exchange, informatics, Systems Analysis

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References


de Lusignan S, Cashman J, Poh N, Michalakidis G, Mason A and Krause P et al. Conducting requirements analyses for research using routinely collected health data: a model driven approach. Studies in Health Technology and Informatics 2012;180:1105–7. PMid:22874368.

Medlock S, Wyatt JC, Patel VL, Shortliffe EH and Abu-Hanna A. Modeling information flows in clinical decision support: key insights for enhancing system effectiveness. Journal of the American Medical Informatics Association 2016;pii: ocv177. http://dx.doi.org/10.1093/jamia/ocv177.

Kumarapeli P, de Lusignan S, Ellis T and Jones B. Using Unified Modelling Language (UML) as a process-modelling technique for clinical-research process improvement. Medical Informatics and the Internet in Medicine 2007;32(1):51–64. http://dx.doi.org/10.1080/14639230601097705. PMid:17365645.

de Lusignan S, Krause P, Michalakidis G, Vicente MT, Thompson S and McGilchrist M et al. Business Process Modelling is an Essential Part of a Requirements Analysis. Contribution of EFMI Primary Care Working Group. Yearbook of Medical Informatics 2012;7:34–43. PMid:22890339.

Carr H, de Lusignan S, Liyanage H, Liaw ST, Terry A and Rafi I. Defining dimensions of research readiness: a conceptual model for primary care research networks. BioMed Central Family Practice 2014;15:169. http://dx.doi.org/10.1186/s12875-014-0169-6.

Caspers BA and Pickard B. Value-based resource management: a model for best value nursing care. Nursing Administration Quarterly 2013;37(2):95-104. doi: http://dx.doi.org/10.1097/NAQ.0b013e3182869e17.

Boston Scientific. Uncovering Asthma Survey 2015. Available from https://www.bostonscientific.com/content/dam/bostonscientific/BT/Uncovering_Asthma/PDF_Documents/The%20Uncovering%20Asthma%20Report_ENG_230915.pdf.

Masoli M, Fabian D, Holt S, Beasley R and Global Initiative for Asthma Program. The global burden of asthma: executive summary of the GINA Dissemination Committee report. Allergy 2004;59(5):469–78. http://dx.doi.org/10.1111/j.1398-9995.2004.00526.x. PMid:15080825.

Looijmans-van den Akker I, van Luijn K and Verheij T. Overdiagnosis of asthma in children in primary care: a retrospective analysis. British Journal of General Practice 2016;66(644):e152–7. http://dx.doi.org/10.3399/bjgp16X683965.

Ellis B. Complexity in practice: understanding primary care as a complex adaptive system. Informatics in Primary Care 2010;18(2):135–40. http://dx.doi.org/10.14236/jhi.v18i2.763.

Checkland P. Research Paper. Soft Systems Methodology: A Thirty Year Retrospective. Systems Research and Behavioral Science 2000;17:S11–S58

Berg M. Rationalizing Medical Work: Decision-Support Techniques and Medical Practices. Massachusetts, USA: MIT Press Cambridge. 1997. ISBN:0262024179

Berg M, Aarts J and van der Lei J. ICT in health care: sociotechnical approaches. Methods of Information in Medicine 2003;42(4):297–301. PMid:14534625.

Liyanage H, Liaw ST and de Lusignan S. Accelerating the development of an information ecosystem in health care, by stimulating the growth of safe intermediate processing of health information (IPHI). Inform Prim Care 2012;20(2):81–6. PMid:23710772.

Chen, Yu-Liu. “Data Flow Diagram.” Modeling and Analysis of Enterprise and Information Systems. Berlin, Heidelberg:Springer. 2009. 85–97.

Rumbaugh J, Jacobson I and Booch G. The Unified Modeling Language Reference Manual. 1999

Object Management Group, Inc. BPMN Specification – Business Process Model and Notation. Available from www.bpmn.org. Accessed on 24 February 2016.

Iwayama K, Hirata Y, Takahashi K, Watanabe K, Aihara K and Suzuki H. Characterizing global evolutions of complex systems via intermediate network representations. Scientific Reports 2012;2:423. http://dx.doi.org/10.1038/srep00423.

MOCHA. http://www.childhealthservicemodels.eu/. Accessed on 24 February 2016.

National Asthma Education and Prevention Program. Expert Panel Report 3 (EPR-3): Guidelines for the Diagnosis and Management of Asthma-Summary Report 2007. Journal of Allergy and Clinical Immunology 2007;120(5 Suppl):S94–138. Erratum in: Journal of Allergy and Clinical Immunology. 2008;121(6):1330. http://dx.doi.org/10.1016/j.jaci.2007.09.029. PMid:17983880.




DOI: http://dx.doi.org/10.14236/jhi.v23i1.863

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