Recent decades have seen increasing recognition of the importance of patient involvement during patient-professional interactions and promotion of preventative and long term approaches to healthcare for those with long-term conditions. The concepts of 'shared decision making' and 'personalised care planning' have both been advocated by patient groups, policy-makers, professional bodies and academia as best practice. During shared decision making, patients and healthcare professionals work in equal partnership to decide the best course of action. Shared decision making is a central tenet of personalised care planning, as it aims to foster partnerships between patients and healthcare professionals when making decisions, but personalised care planning also describes an overall approach to healthcare that is forward-planning and preventative, rather than episodic and reactive. Despite the breadth of support for such approaches, in reality they are not routinely adopted. Multiple Sclerosis (MS) is a heterogeneous neurodegenerative long term condition, which is unpredictable with limited treatments available. Such uncertainty and complexity position MS as an interesting long term condition to explore decisional and information needs. This doctoral research comprises of three methods stages. Firstly, two systematic reviews assessing the effectiveness of personalised care planning for people with long-term conditions and people with MS were carried out. Secondly, 22 in-depth semi-structured qualitative interviews were carried out with people with MS across the UK to explore experiences of decision making and interactions with healthcare professionals. Purposive sampling was carried out and data saturation determined sample size. A modified grounded theory approach was used and thematic analysis of interview data was carried out. Lastly, a series of structured qualitative interviews were carried out with 6 consultant neurologists. This stage was iterative in that problematic areas identified during analysis of interview data from stage 2 were presented to neurologists in infographic form to further examine issues raised. Framework analysis was carried out on neurologist interview data to examine their interpretations and potential solutions. Although there appears to be some evidence demonstrating that personalised care planning is effective for people with long term conditions, such favourable effects were not demonstrated in the context of MS. Based on the findings from the systematic reviews it is unclear whether personalised care planning is effective for people with MS and there is a clear gap in the literature examining this. Findings from the interview stages suggest there are key areas which are lacking in terms of information and decisional support. Such areas included the type and amount of information around the time of diagnosis, support when choosing disease modifying drugs and discussions about approaches outside mainstream medicine. Findings from neurologist interview data corroborated those from MS interview data, but through examination of issues raised it also highlighted some of the complexities and challenges of involving patients and enacting shared decision making in reality. This research identified key areas that require improvement for people with MS in terms of provision of the information and decisional support. Although in theory personalised care planning and shared decision making are positioned as best practice, in reality it is unclear whether they are effective or appropriate for people with MS. The way in which such approaches are enacted are complex and require careful consideration. Potential barriers and pitfalls identified within this study suggest a lack of clarity in how to respond to challenges and further investigation into how patient involvement is enacted is needed.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:729847 |
| Date | January 2017 |
| Creators | Eccles, Abigail |
| Contributors | Ryan, Sara ; Locock, Louise |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://ora.ox.ac.uk/objects/uuid:ffcb33fe-5d5c-4e85-b88d-a05ef0f5dd39 |
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