Breast cancer is one of the most frequently occurring cancers amongst women throughout the world. After the diagnosis of the disease, monitoring its progression is important in predicting the chances of long term survival of patients. The Nottingham Prognostic Index (NPI) is one of the most common indices used to categorise the patients into different groups depending upon the severity of the disease. One of the key factors of this index is cancer grade which is determined by pathologists who examine cell samples under a microscope. This manual method has a higher chance of false classification and may lead to incorrect treatment of patients. There is a need to develop automated methods that employ advanced computational methods to help pathologists in making a decision regarding the classification of breast cancer grade. Fourier transform infra-red spectroscopy (FTIR) is one of the relatively new techniques that has been used for diagnosis of various cancer types with advanced computational methods in the literature. In this thesis we examine the use of advanced fuzzy methods with the FTIR spectral data sets to develop a model prototype that can help clinicians with breast cancer grading. Initial work is focussed on using the commonly used clustering algorithms k-means and fuzzy c-means with principal component analysis on different cancer spectral data sets to explore the complexities within them. After that, a novel model based on Type-II fuzzy logic is developed for use on a complex breast cancer FTIR spectral data set that can help clinicians classify breast cancer grades. The data set used for the purpose consists of multiple cases of each grade. We consider two types of uncertainty, one within the spectra of a single case of a grade (intra -case) and other when comparing it with other cases of same grade (inter-case). Features have been extracted in terms of interval data from various peaks and troughs. The interval data from the features has been used to create Type-I fuzzy sets for each case. After that the Type-I fuzzy sets are combined to create zSlices based General Type-II fuzzy sets for each feature for each grade. The created benchmark fuzzy sets are then used as prototypes for classification of unseen spectral data. Type-I fuzzy sets are created for unseen spectral data and then compared against the benchmark prototype Type-II fuzzy sets for each grade using a similarity measure. The best match based on the calculated similarity scores is assigned as the resultant grade. The novel model is tested on an independent spectral data set of oral cancer patients. Results indicate that the model was able to successfully construct prototype fuzzy sets for the data set, and provide in-depth information regarding the complexities of the data set as well as helping in classification of the data.

Clinical pathways define patient journeys with medical devices often utilised to diagnose and treat patients. In the contexts of screening and diagnosis, devices are utilised in the form of investigations and tests. They are utilised to detect preventable medical conditions in otherwise healthy individuals and to determine the cause of symptoms in patients presenting symptoms. Patient experiences of investigations and tests vary depending on the healthcare situation, investigation or test, and requirements, expectations and physical experiences of patients before, during and after investigations and tests. Information can be a valuable resource to inform, support and guide patients, and to contribute to quality patient experiences. However, this can only be achieved if information meets patient needs and preferences. This was the basis of the thesis, which took a user centred approach to the design, development and implementation of patient information. Two studies were conducted focussing on understanding attitudes towards investigations and tests, and informational needs and preferences. The first study examined attitudes towards different types of diagnostic procedure and the second examined attitudes towards screening for a vascular condition. Information was valuable in the former to inform about diagnostic procedures and patients’ physical involvement with them, and in the latter to inform about the medical condition, screening for the condition, the screening procedure, the benefits of being screened, and the risks of being or not being screened. Both studies also established factors affecting attitudes, providing a constructive understanding of attitudes. Ten factors were established that affected attitudes towards diagnostic procedures of which physical involvement, trust, familiarity and purpose were the most influential factors. Fifteen factors were established that affected attitudes towards screening of which benefits and risks, referring to personal benefits and risks, were the most influential factors. The established factors inspired a user centred design concept for patient information – a ‘factors based approach’ to the design of patient information. The factors based approach to the design of patient information is theoretical and consists of including and organising information based on factors. This approach was examined in two studies, which involved re-designing a patient information resource for an invasive investigation by applying appropriate factors established in the first two studies to it and examining and comparing it with the original information resource. The original resource was based on a standardised presentation of information for the investigation. The factors based resource was quantitatively no better nor worse than the standard resource; however, qualitative data found it had features that were important for its usability, which seemed to make it easier to understand compared to the standard resource. These findings demonstrated the potential of the factors based approach to the design of patient information, which led to the development of patient information guidelines. Patient information guidelines are provided for diagnostic procedures and screening. The guidelines represent the essence of the thesis and its work, and the contribution it has made to knowledge. They combine substantial data from four studies and it is hoped the guidelines assist information designers and others involved in patient information. The guidelines also aim to contribute to quality patient experiences through better meeting patient informational needs and preferences. Since the factors based approach to the design of patient information is a novel concept and the patient information guidelines are a draft, further research is recommended to better understand the potential of the factors based approach and to further develop and refine the guidelines. The guidelines have been made publicly available to use as a separate document and for further dissemination, and can be accessed and downloaded from the following link: https://db.tt/e6BQeJuu

The increasing use of technology in delivering clinical services brings substantial benefits to the healthcare industry. At the same time, it introduces potential new complications to clinical workflows that generate new risks and hazards with the potential to affect patients’ safety. These workflows are safety critical and can have a damaging impact on all the involved parties if they fail. Due to the large number of processes included in the delivery of a clinical service, it can be difficult to determine the individuals or the processes that are responsible for adverse events. Using methodological approaches and automated tools to carry out an analysis of the workflow can help in determining the origins of potential adverse events and consequently help in avoiding preventable errors. There is a scarcity of studies addressing this problem; this was a partial motivation for this thesis. The main aim of the research is to demonstrate the potential value of computer science based dependability approaches to healthcare and in particular, the appropriateness and benefits of these dependability approaches to overall clinical workflows. A particular focus is to show that model-based safety analysis techniques can be usefully applied to such areas and then to evaluate this application. This thesis develops the SAFE-FLOW approach for safety analysis of clinical workflows in order to establish the relevance of such application. SAFE-FLOW detailed steps and guidelines for its application are explained. Then, SAFE-FLOW is applied to a case study and is systematically evaluated. The proposed evaluation design provides a generic evaluation strategy that can be used to evaluate the adoption of safety analysis methods in healthcare. It is concluded that safety of clinical workflows can be significantly improved by performing safety analysis on workflow models. The evaluation results show that SAFE-FLOW is feasible and it has the potential to provide various benefits; it provides a mechanism for a systematic identification of both adverse events and safeguards, which is helpful in terms of identifying the causes of possible adverse events before they happen and can assist in the design of workflows to avoid such occurrences. The clear definition of the workflow including its processes and tasks provides a valuable opportunity for formulation of safety improvement strategies.

Background For epidemiological research purposes structured data provide identifiable and immediate access to the information that has been recorded, however, many quantitative recordings in electronic medical records are unstructured. This means researchers have to manually identify and extract information of interest. This is costly in terms of time and money and with access to larger amounts of electronically stored data this approach is becoming increasingly impractical. Method Two programmatic methods were developed to extract and classify numeric quantities and identify attributes from unstructured dosage instructions and clinical comments from The Health Improvement Network (THIN) database. Both methods are based on frequently occurring patterns of recording from which models were formed. Dosage instructions: Automated coding was achieved through the interpretation of a representative set of language phrases with identifiable traits. The dosage data table was automatically recoded and assessed for accuracy and coverage of a daily dosage value, then assessed in the context of 146 commonly prescribed medications. Clinical comments: Automated coding was achieved through the identification of a representative set of text and/or Read code qualifications. The model was initially trained on THIN data for a wide range of numeric health indicators, then tested for generalizability using comments from an alternative source and assessed for accuracy, sensitivity, and specificity using a subset of 12 commonly recorded health indicators. Results Dosage instructions: The coverage of a daily dosage value within the dosage data table was increased from 42.1% to 84.8% coverage with an accuracy of 84.6%. For the 146 medications assessed, on a per-unique-instruction basis, the coverage was 79.7% on average with an accuracy of 95.4%. On an all-recorded-instructions basis the weighted coverage was 65.9% on average with an accuracy of 99.3%. Clinical comments: For all 12 of the health indicators assessed the automated extraction achieved a specificity of >98% and an accuracy of >99%. The sensitivity was >96% for 8 of the indicators and between 52-88% for the other indicators. Conclusion Dosage instructions: The automated coding has improved the quantitative and qualitative summary for dosage instructions within THIN resulting in a substantial increase in the quantity of data available for pharmaco-epidemiological research. Clinical comments: The sensitivity of the extraction method is dependent on the consistency of recording patterns, which in turn was dependent on the ability to identify the differing patterns of qualification during training.

Healthcare is changing, correction ... healthcare is in need of change. The population ageing, the increase in chronic and heart diseases and just the increase in population size will overwhelm the current hospital-centric healthcare. There is a growing interest by individuals to monitor their own physiology. Not only for sport activities, but also to control their own diseases. They are changing from the passive healthcare receiver to a proactive self-healthcare taker. The focus is shifting from hospital centred treatment to a patient-centric healthcare monitoring. Continuous, everyday, wearable monitoring and actuating is part of this change. In this setting, sensors that monitor the heart, blood pressure, movement, brain activity, dopamine levels, and actuators that pump insulin, 'pump' the heart, deliver drugs to specific organs, stimulate the brain are needed as pervasive components in and on the body. They will tend for people's need of self-monitoring and facilitate healthcare delivery. These components around a human body that communicate to sense and act in a coordinated fashion make a Body Area Network (BAN). In most cases, and in our view, a central, more powerful component will act as the coordinator of this network. These networks aim to augment the power to monitor the human body and react to problems discovered with this observation. One key advantage of this system is their overarching view of the whole network. That is, the central component can have an understanding of all the monitored signals and correlate them to better evaluate and react to problems. This is the focus of our thesis. In this document we argue that this multi-parameter correlation of the heterogeneous sensed information is not being handled in BANs. The current view depends exclusively on the applica- tion that is using the network and its understanding of the parameters. This means that every application will oversee the BAN's heterogeneous resources managing them directly without taking into consideration other applications, their needs and knowledge. There are several physiological correlations already known by the medical field. Correlating blood pressure and cross sectional area of blood vessels to calculate blood velocity, estimating oxygen delivery from cardiac output and oxygen saturation, are such examples. This knowledge should be available in a BAN and shared by the several applications that make use of the network. This architecture implies a central component that manages the knowledge and the resources. And this is, in our view, missing in BANs. Our proposal is a middleware layer that abstracts the underlying BAN's resources to the applica- tion, providing instead an information model to be queried. The model describes the correlations for producing new information that the middleware knows about. Naturally, the raw sensed data is also part of the model. The middleware hides the specificities of the nodes that constitute the BAN, by making available their sensed production. Applications are able to query for information attaching requirements to these requests. The middleware is then responsible for satisfying the requests while optimising the resource usage of the BAN.Our architecture proposal is divided in two corresponding layers, one that abstracts the nodes' hardware (hiding node's particularities) and the information layer that describes information available and how it is correlated. A prototype implementation of the architecture was done to illustrate the concept.

Production and consumption of text, image and video content about both diabetes and common mental health disorders (CMHDs), by individuals and organisations, has become commonplace since the widespread adoption of social media. Despite the increasing importance of these online spaces for health-related discussion few studies have fully explored people’s experiences of drawing on social media content around either diabetes or CMHDs. The aim of this study was to explore the multiple ways young adults engage with health-related content online and develop an understanding of how social media are used for health information and communication. A further aim was to explore the areas of convergence and divergence between professional producers’ perspectives on online resources about diabetes and CMHDs and prospective users’ perspectives. To explore these issues, a qualitative study was developed. Forty young adults, aged between 18 and 30 years, and six professional producers took part in semi-structured interviews. The key findings of the study reflect the increasing prominence of health-related user-generated content online. While continued reliance on search-engines for locating relevant content was evident, some participants discussed accessing health-related content as part of their everyday social media activity. Further, participants’ perceptions and experiences of support from family, friends and formal health services appeared to relate to their online practices: those who described least supportive resources offline discussed engaging most actively in production and consumption of health-related user-generated content. Participants also discussed what limited their production of health-related content, suggesting that production of content related to diabetes or CMHDs could compromise their presentation of self online. Disjunctures were evident between the perspectives of producers and potential users, with producers prioritising dissemination of generic information and young adults emphasising the consumption of tailored content. The findings of the study suggest key opportunities for exploiting the potential of social media to engage with users but highlight potential barriers to some individuals’ engagement.

Several European countries, including the UK, are investing in large-scale telehealthcare pilots, to thoroughly evaluate the benefits of telehealthcare. Due to the high level of risk associated with such projects, it becomes desirable to be able to predict the success of telehealthcare systems in potential deployments, in order to inform investment and help save resources. An important factor for the success of any telehealthcare deployment is usability, as it helps to achieve the benefits of the technology through increased productivity, decreased error rates, and better acceptance. In particular, efficiency, one of the characteristics of usability, should be seen as a central measure for success, as the timely care of a high number of patients is one of the important claims of telehealthcare. Despite the recognized importance of usability, it is seen as secondary in the design of telehealthcare systems. The resulting problems are difficult to predict due to the heterogeneity of deployment contexts. This thesis proposes the automation of usability evaluation through the use of modelling and simulation techniques. It describes a generic methodology which can guide a modeller in reusing models for predicting characteristics of usability within different deployment sites. It also describes a modelling approach which can be used together with the methodology, to run in parallel a user model, inspired from a cognitive architecture, and a system model, represented as a basic labelled transition system. The approach simulates a user working with a telehealthcare system, and within her environment, to predict the efficiency of the system and work process surrounding it. The modeller can experiment with different inputs to the models in terms of user profile, workload, ways of working, and system design, to model different potential- real or hypothetical- deployments, and obtain efficiency predictions for each. A comparison of the predictions helps analyse the effects on efficiency of changes in deployments. The work is presented as an experimental investigation, but emphasises the great potential of modelling and simulation for helping to inform investment, help reduce costs, mitigate risks and suggest changes that would be necessary for improving the usability, and therefore success or telehealthcare deployments. My vision is that, if used commercially, the approaches presented in this thesis could help reduce risks for scaling up telehealthcare deployments.

Studies on electronic patient medication record (ePMR) systems that are used in community pharmacy in England have focused primarily on the ability of these systems to highlight potentially hazardous co-prescriptions and prevent clinical hazards and harm to patients. As such, there is a scarcity of literature on the use of ePMR systems, other safety functionality of the systems and user responses to alerts. This thesis aims to fill this gap by examining the functionality of ePMR systems used in community pharmacy in England, focusing on safety features and alerts. This research was conducted in England between July 2010 and July 2013. Evidence for the effectiveness of safety features and alerts in ePMR systems during the dispensing process was evaluated through a systematic review of the literature. Stakeholder perspectives of ePMR systems’ functionality were then obtained through qualitative interviews. The performance of ePMR systems licensed for the electronic prescription service (release 2) in the community pharmacy setting were then assessed using a simulated observational testing approach. Ethnographically informed observations in community pharmacies were subsequently used to study how community pharmacy professionals use ePMR systems and manage alerts in practice. The systematic review included five studies - three randomised controlled trials and two before-after studies, with drug-drug interaction (1), drug-laboratory (2), drug-pregnancy (1) and drug-age (1) alerts. The review revealed that ePMR systems in conjunction with embedded safety features are effective in picking up clinical hazards at the point of dispensing. However, there are problems of false alerts and inconsistencies in alert management. Empirical findings indicate that there are significant issues with the way ePMR systems and alerts are designed and used. Thirty participants took part in the qualitative interviews - community pharmacy professionals (13), health care policy makers (5), legal practitioners specialising in pharmacy (3), ePMR systems’ software vendors (4) and ePMR systems’ software knowledgebase creators (5). Participants attributed alert ineffectiveness in community pharmacy practice to factors such as lack of harmonisation of alert severity levels in systems, poor alert design, over-presentation of alerts and absence of management advice in alerts. Five unique ePMR systems were evaluated in eight participating pharmacies with a sixth ePMR system assessed in a demonstration setting. The systems’ performance was variable and sub-optimal. The ethnographically informed observations took place in the eight pharmacies where system assessment was conducted. The observations revealed that the current design of ePMR systems and presentation of alerts are limiting the quality of support provided to pharmacists and their support staff. This research is part of a growing body of work on the functionality of ePMR systems, their safety features and alerts indicating that ePMR systems require improvements if they are to effectively support patient safety and consistently deliver better patient outcomes. The findings highlight the need to incorporate patient context into alerting to increase alert relevance. In addition, system vendors need to make use of the evidence in the literature to design effective ePMR systems, alerts and user interfaces. An authoritative body should set the minimum specifications for ePMR systems and alerts, and identify the critical alerts that pharmacy professionals should evaluate at the point of dispensing. Additionally, training of pharmacy professionals in health information and communication technology is required to improve patient safety. This should cover areas such as informatics, human factors, safety culture, clinical decision-making, alert management, risk management and communication. Many of the findings are likely to be relevant to similar medication record systems in ambulatory pharmacies around the world; however, further work is required to understand fully the extent of the issues identified in this research.

Application of Health Informatics (HI) is becoming more pervasive in the Saudi Arabian health organisations (SAHOs) with the aim of exploiting its potential for better healthcare delivery. Yet, to date, the management of HI has not been fully digested in the Saudi health environment. Therefore, adoption of imported models has become a common practice for managing HI. Consequently, most implemented systems fall short of meeting objectives or tackling key existing issues. The aim of the study is to develop a model for HI management that not only deals with key prevailing issues but also should be compatible with the Saudi Arabian health environment. The research contends that the key to success in exploiting the potential of HI is the use of appropriate local models that fully integrate with the Saudi Arabian health environment. The research design was mainly guided by pragmatic philosophy which incorporated both quantitative and qualitative research. It was inductive in nature and used a field research methodology to accomplish the research objectives. Empirical data was collected via questionnaires and interviews in the collaborating health organisations. Literature review, data analyses of the questionnaires and interviews yielded the initial framework for the Health Informatics Management Model (HIMM). A first round evaluation of the HIMM was conducted yielding a revised version. Later, data was also gathered from participants in a second round of evaluating the HIMM. The second round was to reassess the compatibility of HIMM with the Saudi Arabian health organisations, and to update the model in order to match the current application of HI in these organisations. The analysis of the data gleaned from the second stage evaluation yielded a revised (and final) HIMM, contemplated by participants. Based on the above empirical data, the research study introduces the HIMM, the first holistic and systematic HI framework that should enable the Saudi health providers and managers to better comprehend the multi-faceted perspectives that form the HI management paradigm, and guide them in its management. It can allow them to decide how best to manage HI projects in a way that ensures an optimum use of HI resources for effective and efficient delivery of healthcare and services. This work is of considerable utility in the Kingdom of Saudi Arabia and the Gulf States, where HI management and its application are regarded as an area of high priority.

This Ph.D. project started from a broad analysis aiming at investigating the key issues in the development of Information and Communication Technologies (ICT) in the health care sector, with the aim of making an in depth investigation to evaluate the effects of Electronic Medical Record (EMR) implementation on the organizations adopting them. Furthermore the study examined two study settings which have adopted the same EMR system produced by the same provider. This comparative study aims, in particular, to analyse how EMR systems are adopted by different health organizations focusing on the antecedents of the EMR project, on the implementation processes used and on the impacts produced. Diffusion theory, through the lens of socio-technical approach, represents the theoretical framework of the analysis. The research results are based on policy evaluation and case studies. The two hospitals selected for the case study analysis are the Regional Hospital of Local Health Authority in Aosta, Italy and the Royal Infirmary of Edinburgh, Scotland. In conducting the data collection several strategies have been used: documentary analysis, interviews and observations have been carried out. This work provides an overview of the key issues arising over e-health policy development through a comparative analysis of the UK and Italy and provides an insight into how EMR systems are adopted, implemented and evaluated within acute care organizations. The thesis is a comparative international research about the development of e-health and the use of ICT in health care sector. This approach makes a both a theoretical and methodological contribution. By focusing, in particular, on EMR systems, it offers to practitioners and policy makers a better basis of analysing ICT usage and its impacts on health care service delivery.