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The Role and Contribution of the Chief Technology OfficerThe role of the Chief Technology Officer (CTO) came about because of new organisational demands on technology leaders in the 1980s. The initial research objective of this dissertation was to provide a clear scope of activities (a remit) for the CTO role. However, the analysis did not support a generic description for the role. Therefore, the approach taken explores CTO perspectives on technology management priorities when the technology context changes. There is limited literature on the role and contribution of the CTO per se. The resulting gap in the knowledge about the role is amplified by a wide variety of research methods and academic perspectives. From a theoretical point of view, the existing research tends to focus in isolation on the work being done, the working context or the worker (i.e. the CTO). There are studies that consider how the working context is changing, and studies that consider the work of the CTO, for example, the technology management priorities. There are still other studies that consider the attributes of the CTO. In this dissertation, these three perspectives - the working context, the work and the worker - are investigated in an integrated way using a data collection technique called 'personal role mapping' that is based on cognitive mapping. The 'personal role mapping' approach has been developed as part of this work. The evidence collected and analysed shows that the role of the CTO is highly idiosyncratic. This is because the CTO role changes as the organisation adapts in order to compete. Also, the role differs from one industry to another and between organisations within the same industry. To help deal with these variations, a CTO/Context Framework has been derived for use in conjunction with 'technology transition points'. The CTO/Context Framework has 20 sub-elements that support 6 primary elements including, 'technology management infrastructure', 'technology entry/exit points', 'technology business case & funding', 'operational improvement', 'people management' and 'technology business model & strategy'. The CTO can review each element with related sub-elements in anticipation or at the point of a 'technology transition'. This model for the CTO role is proposed as an alternative to a generic 'job description' (remit) for the CTO role. It is intended to be used as a platform for planning and decision-making. Together, the framework and the research approach for mapping an individual's role are offered as a unique contribution to knowledge.

Hospital Acquired Infections (HAI) are a global patient safety challenge, costly to treat, and affect hundreds of millions of patients annually worldwide. It has been shown that the majority of HAI are transferred to patients by caregivers' hands and therefore, can be prevented by proper hand hygiene (HH). However, many factors including cognitive load, cause caregivers to forget to cleanse their hands. Hand hygiene compliance among caregivers remains low around the world. In this thesis I showed that it is possible to build a wearable accelerometer-based HH reminder system to identify ongoing nursing activities with the patient, indicate the high-risk activities, and prompt the caregivers to clean their hands. Eight subjects participated in this study, each wearing five wireless accelerometer sensors on the wrist, upper arms and the back. A pattern recognition approach was used to classify six nursing activities offline. Time-domain features that included mean, standard deviation, energy, and correlation among accelerometer axes were found to be suitable features. On average, 1-Nearest Neighbour classifier was able to classify the activities with 84% accuracy. A novel algorithm was developed to adaptively segment the accelerometer signals to identify the start and stop time of each nursing activity. The overall accuracy of the algorithm for a total of 96 events performed by 8 subjects was approximately 87%. The accuracy was higher than 91% for 5 out of 8 subjects. The sequence of nursing activities was modelled by an 18-state Markov Chain. The model was evaluated by recently published data. The simulation results showed that the high-risk of cross-contamination decreases exponentially by frequency of HH and this happens more rapidly up to 50%-60% hand hygiene rate. It was also found that if the caregiver enters the room with high-risk of transferring infection to the current patient, given the assumptions in this study, only 55% HH is capable of reducing the risk of infection transfer to the lowest level. This may help to prevent the next patient from acquiring infection, preventing an infection outbreak. The model is also capable of simulating the effects of the imperfect HH on the risk of cross-contamination.

Hospital Acquired Infections (HAI) are a global patient safety challenge, costly to treat, and affect hundreds of millions of patients annually worldwide. It has been shown that the majority of HAI are transferred to patients by caregivers' hands and therefore, can be prevented by proper hand hygiene (HH). However, many factors including cognitive load, cause caregivers to forget to cleanse their hands. Hand hygiene compliance among caregivers remains low around the world. In this thesis I showed that it is possible to build a wearable accelerometer-based HH reminder system to identify ongoing nursing activities with the patient, indicate the high-risk activities, and prompt the caregivers to clean their hands. Eight subjects participated in this study, each wearing five wireless accelerometer sensors on the wrist, upper arms and the back. A pattern recognition approach was used to classify six nursing activities offline. Time-domain features that included mean, standard deviation, energy, and correlation among accelerometer axes were found to be suitable features. On average, 1-Nearest Neighbour classifier was able to classify the activities with 84% accuracy. A novel algorithm was developed to adaptively segment the accelerometer signals to identify the start and stop time of each nursing activity. The overall accuracy of the algorithm for a total of 96 events performed by 8 subjects was approximately 87%. The accuracy was higher than 91% for 5 out of 8 subjects. The sequence of nursing activities was modelled by an 18-state Markov Chain. The model was evaluated by recently published data. The simulation results showed that the high-risk of cross-contamination decreases exponentially by frequency of HH and this happens more rapidly up to 50%-60% hand hygiene rate. It was also found that if the caregiver enters the room with high-risk of transferring infection to the current patient, given the assumptions in this study, only 55% HH is capable of reducing the risk of infection transfer to the lowest level. This may help to prevent the next patient from acquiring infection, preventing an infection outbreak. The model is also capable of simulating the effects of the imperfect HH on the risk of cross-contamination.