Models of risk generally struggle to cope with the complexities of healthcare, and in the context of medical equipment, it is apparent that several categories of ‘risk’ can be identified which are active concurrently. From previous development of a clinical risk simulation model within a Critical Care environment, a specific implementation of fuzzy logic was found to provide a means of developing a ‘risk engine’ which referenced contributing factors and preventative factors of risk in the clinical environment. Components of this ‘risk engine’ model have been applied to the task of classification of risk associated with medical equipment. This in turn allows priorities to be identified in relation to management of a diverse equipment portfolio.
|Title of host publication||IPEM Report 110: Quality in Clinical Engineering|
|Publisher||Institute of Physics and Engineering in Medicine|
|Publication status||Published - 2015|
Bibliographical noteThis chapter is reproduced with permission of the publisher Institute of Physics and Engineering in Medicine From report 110: Quality in Clinical Engineering, ISBN 978 1 903613 58 0. The report is available to non-members of IPEM at £50.00 from http://www.ipem.ac.uk/Publications/IPEMReportSeries/AvailablePublications.aspx .
- risk management
- medical devices
- patient safety
- fuzzy logic
FingerprintDive into the research topics of 'Profiling of medical equipment risk using fuzzy logic'. Together they form a unique fingerprint.
- Centre for Future Transport and Cities - Associate Professor Academic
Person: Teaching and Research