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Background: Detection of prostate cancer in primary care relies on Digital Rectal Examination (DRE) and Prostate Specific Antigen (PSA), both of which have significant limitations. Magnetic Resonance Imaging (MRI) has emerged as a diagnostic test for prostate cancer that is more accurate than existing tests but is currently only used in secondary care. This PhD aimed to examine the potential impact of prostate MRI on the diagnosis of prostate cancer in primary care. Methods: I performed systematic reviews with narrative syntheses to examine the evidence on patient centred outcomes (PCOs) from diagnostic tests for prostate cancer, including MRI, and the cost-effectiveness of prostate cancer diagnostic pathways that incorporate pre-biopsy MRI. I undertook a qualitative study employing one-to-one interviews of patients who had undergone prostate MRI and GPs who had recently referred men with suspected prostate cancer for diagnostic testing to explore the acceptability of prostate MRI as a diagnostic test and experiences of the prostate cancer diagnostic pathway. I completed an early economic evaluation of primary care prostate cancer diagnostic pathways incorporating prostate MRI using decision analytic modelling informed by a linked data approach. Results: Prostate MRI and MRI-guided biopsy have more favourable PCOs compared to standard prostate biopsy techniques. Prostate MRI met most key constructs of acceptability for patients, whilst GPs had a spectrum of knowledge and understanding of prostate MRI as a diagnostic test for prostate cancer. Published evidence suggests MRI-based prostate cancer diagnostic pathways are cost-effective, although no studies incorporated primary care data. Modelling suggests implementing prostate MRI in primary care could reduce costs involved with prostate cancer diagnosis, without a significant utility detriment for patients. Conclusion: Incorporating MRI into primary care as a diagnostic test for prostate cancer could reduce costs for the NHS and is acceptable to patients. Prior to implementation in primary care, feasibility, comparable diagnostic accuracy, GPs' clinical knowledge, and improved outcomes for patients must be demonstrated.

Over 275,000 women died of pregnancy related causes in 2015. Most occur in resource-poor settings and are preventable. This study aimed to improve the working lives of maternity healthcare workers in Malawi to enable delivery of higher quality care, using Appreciative Inquiry (AI); a positive-focused, participatory action cycle. Following a systematic review and narrative synthesis of AI, an ethnographic study and Interpretative Phenomenological perspective were utilised to understand working lives. Before the intervention was implemented, working lives were assessed through validated questionnaires for staff and patient satisfaction surveys. AI has been used in healthcare, but little empirical evidence for its effectiveness exists. Staff wanted to do a good job, but were confined by a lack of resources, knowledge and support. The longitudinal survey of staff showed significant improvements in general wellbeing and home-work interface, and patient satisfaction improved. AI also improved staff relationships and made work easier and happier. Qualitative work suggested this was because staff were working better together, underpinned by everyone meeting together. From these findings a theory of change was developed. AI showed great promise. However, further research, in the form of a large-scale trial, is needed to empirically demonstrate the effectiveness of AI in healthcare.