From Data to Knowledge in Secondary Health Care Databases

The advent of big data in health care is a topic receiving increasing attention worldwide. In the UK, over the last decade, the National Health Service (NHS) programme for Information Technology has boosted big data by introducing electronic infrastructures in hospitals and GP practices across the country. This ever growing amount of data promises to expand our understanding of the services, processes and research. Potential bene�ts include reducing costs, optimisation of services, knowledge discovery, and patient-centred predictive modelling. This thesis will explore the above by studying over ten years worth of electronic data and systems in a hospital treating over 750 thousand patients a year. The hospital's information systems store routinely collected data, used primarily by health practitioners to support and improve patient care. This raw data is recorded on several di�erent systems but rarely linked or analysed. This thesis explores the secondary uses of such data by undertaking two case studies, one on prostate cancer and another on stroke. The journey from data to knowledge is made in each of the studies by traversing critical steps: data retrieval, linkage, integration, preparation, mining and analysis. Throughout, novel methods and computational techniques are introduced and the value of routinely collected data is assessed. In particular, this thesis discusses in detail the methodological aspects of developing clinical data warehouses from routine heterogeneous data and it introduces methods to model, visualise and analyse the journeys that patients take through care. This work has provided lessons in hospital IT provision, integration, visualisation and analytics of complex electronic patient records and databases and has enabled the use of raw routine data for management decision making and clinical research in both case studies

Beyond problem identification: valuing methods in a ‘system usability practice’

Historically, usability evaluation methods (UEMs) have been evaluated on their capability for problem identification. However, the relevance of this approach has been questioned for applied usability work. To investigate alternative explanations of what is important for method use a grounded theory of usability practitioners was developed (9 interviews from the website domain and 13 in the safety-critical domain). The analysis proceeded in bottom-up and top-down stages. The bottom-up stages produced insight from the data in an exploratory and inductive manner. This highlighted the importance of contextual factors and the need for system descriptions: UEM adoption and adaptation cannot be fully understood devoid of context. The top-down stages used Distributed Cognition and Resilience Engineering conceptual frameworks as leverage for exploring the data in a deductive manner. These were chosen for their functional descriptions of systems. To illustrate the importance of context we describe three models: 1) where previous research has highlighted the downstream utility of UEMs we expand the metaphor to consider the landscape through which the stream flows, where the landscape represents the project’s context; 2) where information propagation and transformation in a project is influenced by social, information flow, artefact, physical and evolutionary factors; and 3) where the functional couplings between parts of the system of usability practice can be monitored and managed to positively resonate with each other, thereby improving the performance of the system overall. The concept of ‘Positive Resonance’ is introduced to describe how practitioners adapt to the context to maximise their impact under constrained resources. The functional couplings are described in a functional resonance model of HCI practice. This model is validated by interviewees and other practitioners outside of the study. This research shows that problem identification is limited for valuing UEMs. Instead, functional couplings of UEMs should be considered to improve system performance, which influence UEM adoption and adaptation in practice