ISSN: 0268-9235
Currently published as: Journal of Health Organization and Management
Incorporates: Health Manpower Management
Online from: 1986
Subject Area: Health Care Management/Healthcare
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| Title: | Using geographical information systems for management of back-pain data |
|---|---|
| Author(s): | G. Ghinea, (Department of Information Systems and Computing, Brunel University, Uxbridge, UK), D. Gill, (Department of Information Systems and Computing, Brunel University, Uxbridge, UK), A. Frank, (Department of Rehabilitation Medicine and Rheumatology, Northwick Park Hospital, UK), L.H. de Souza, (Department of Health and Social Care, Brunel University, Osterley, UK) |
| Citation: | G. Ghinea, D. Gill, A. Frank, L.H. de Souza, (2002) "Using geographical information systems for management of back-pain data", Journal of Management in Medicine, Vol. 16 Iss: 2/3, pp.219 - 237 |
| Keywords: | Data collection, Geographical information systems, Health care, Management |
| Article type: | Research paper |
| DOI: | 10.1108/02689230210434952 (Permanent URL) |
| Publisher: | MCB UP Ltd |
| Abstract: | In the medical world, statistical visualisation has largely been confined to the realm of relatively simple geographical applications. This remains the case, even though hospitals have been collecting spatial data relating to patients. In particular, hospitals have a wealth of back pain information, which includes pain drawings, usually detailing the spatial distribution and type of pain suffered by back-pain patients. Proposes several technological solutions, which permit data within back-pain datasets to be digitally linked to the pain drawings in order to provide methods of computer-based data management and analysis. In particular, proposes the use of geographical information systems (GIS), up till now a tool used mainly in the geographic and cartographic domains, to provide novel and powerful ways of visualising and managing back-pain data. A comparative evaluation of the proposed solutions shows that, although adding complexity and cost, the GIS-based solution is the one most appropriate for visualisation and analysis of back-pain datasets. |
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