Antimicrobial stewardship in Ireland, with a focus on long term care facilities

Background: Antimicrobial resistance is a major public health concern, and its increasing incidence in the Long Term Care Facility (LTCF) setting warrants attention (1). The prescribing of antimicrobials in this setting is often inappropriate and higher in Ireland than the European average (2). The aim of the study was to generate an evidence base for the factors influencing antimicrobial prescribing in LTCFs and to investigate Antimicrobial Stewardship (AMS) strategies for LTCFs. Methods: An initial qualitative study was conducted to determine the factors influencing antimicrobial prescribing in Irish LTCFs. This allowed for the informed implementation of an AMS feasibility study in LTCFs in the greater Cork region. Hospital AMS was also investigated by means of a national survey. A study of LTCF urine sample antimicrobial resistance rates was conducted in order to collate information for incorporation into future LTCF AMS initiatives. Results: The qualitative interviews determined that there are a multitude of factors, unique to the LTCF setting, which influence antimicrobial prescribing. There was a positive response from the doctors and nurses involved in the feasibility study as they welcomed the opportunity to engage with AMS and audit and feedback activities. While the results did not indicate a significant change in antimicrobial prescribing over the study period, important trends and patterns of use were detected. The antimicrobial susceptibility of LTCF urine samples compared to GPs samples found that there was a higher level of antimicrobial resistance in LTCFs. Conclusion: This study has made an important contribution to the development of AMS in LTCFs. The complexity of care and healthcare organisation, and the factors unique to LTCFs must be borne in mind when developing quality improvement strategies.

A facilities maintenance management process based on degradation prediction using sensed data

Energy efficiency and user comfort have recently become priorities in the Facility Management (FM) sector. This has resulted in the use of innovative building components, such as thermal solar panels, heat pumps, etc., as they have potential to provide better performance, energy savings and increased user comfort. However, as the complexity of components increases, the requirement for maintenance management also increases. The standard routine for building maintenance is inspection which results in repairs or replacement when a fault is found. This routine leads to unnecessary inspections which have a cost with respect to downtime of a component and work hours. This research proposes an alternative routine: performing building maintenance at the point in time when the component is degrading and requires maintenance, thus reducing the frequency of unnecessary inspections. This thesis demonstrates that statistical techniques can be used as part of a maintenance management methodology to invoke maintenance before failure occurs. The proposed FM process is presented through a scenario utilising current Building Information Modelling (BIM) technology and innovative contractual and organisational models. This FM scenario supports a Degradation based Maintenance (DbM) scheduling methodology, implemented using two statistical techniques, Particle Filters (PFs) and Gaussian Processes (GPs). DbM consists of extracting and tracking a degradation metric for a component. Limits for the degradation metric are identified based on one of a number of proposed processes. These processes determine the limits based on the maturity of the historical information available. DbM is implemented for three case study components: a heat exchanger; a heat pump; and a set of bearings. The identified degradation points for each case study, from a PF, a GP and a hybrid (PF and GP combined) DbM implementation are assessed against known degradation points. The GP implementations are successful for all components. For the PF implementations, the results presented in this thesis find that the extracted metrics and limits identify degradation occurrences accurately for components which are in continuous operation. For components which have seasonal operational periods, the PF may wrongly identify degradation. The GP performs more robustly than the PF, but the PF, on average, results in fewer false positives. The hybrid implementations, which are a combination of GP and PF results, are successful for 2 of 3 case studies and are not affected by seasonal data. Overall, DbM is effectively applied for the three case study components. The accuracy of the implementations is dependant on the relationships modelled by the PF and GP, and on the type and quantity of data available. This novel maintenance process can improve equipment performance and reduce energy wastage from BSCs operation.