The Responsiveness, Content Validity, and Convergent Validity of the Measure Yourself Concerns and Wellbeing (MYCaW) Patient-Reported Outcome Measure

Objective Measure Yourself Concerns and Wellbeing (MYCaW) is a patient-centred questionnaire that allows cancer patients to identify and quantify the severity of their ‘Concerns’ and Wellbeing, as opposed to using a pre-determined list. MYCaW administration is brief and aids in prioritising treatment approaches. Our goal was to assess the convergent validity and responsiveness of MYCaW scores over time, the generalisability of the existing qualitative coding framework in different complementary and integrative healthcare settings and content validity. Methods Baseline and 6-week follow-up data (n=82) from MYCaW and FACIT-SpEx questionnaires were collected for a service evaluation of the ‘Living Well With The Impact of Cancer’ course at Penny Brohn Cancer Care. MYCaW construct validity was determined using Spearman's Rank Correlation test, and responsiveness indices assessed score changes over time. The existing qualitative coding framework was reviewed using a new dataset (n=158) and coverage of concern categories compared to items of existing outcome measures. Results Good correlation between MYCaW and FACIT-SpEx score changes were achieved (r= -0.57, p≥0.01). MYCaW Profile and Concern scores were highly responsive to change: SRM=1.02 and 1.08; effect size=1.26 and 1.22. MYCaW change scores showed the anticipated gradient of change according to clinically relevant degrees of change. Categories including ‘Spirituality’, ‘weight change’ and ‘practical concerns’ were added to the coding framework to improve generalisability. Conclusions MYCaW scores were highly responsive to change, allowing personalized patient outcomes to be quantified; the qualitative coding framework is generalisable across different oncology settings and has broader coverage of patient-identified concerns compared with existing cancer-related patient-reported outcome measures.

Organisational and systems factors impacting on patient safety in acute care organisations: lessons from four multi-site research studies

Background Patient safety is concerned with preventable harm in healthcare, a subject that became a focus for study in the UK in the late 1990s. How to improve patient safety, presented both a practical and a research challenge in the early 2000s, leading to the eleven publications presented in this thesis. Research question The overarching research question was: What are the key organisational and systems factors that impact on patient safety, and how can these best be researched? Methods Research was conducted in over 40 acute care organisations in the UK and Europe between 2006 and 2013. The approaches included surveys, interviews, documentary analysis and non-participant observation. Two studies were longitudinal. Results The findings reveal the nature and extent of poor systems reliability and its effect on patient safety; the factors underpinning cases of patient harm; the cultural issues impacting on safety and quality; and the importance of a common language for quality and safety across an organisation. Across the publications, nine key organisational and systems factors emerged as important for patient safety improvement. These include leadership stability; data infrastructure; measurement capability; standardisation of clinical systems; and creating an open and fair collective culture where poor safety is challenged. Conclusions and contribution to knowledge The research presented in the publications has provided a more complete understanding of the organisation and systems factors underpinning safer healthcare. Lessons are drawn to inform methods for future research, including: how to define success in patient safety improvement studies; how to take into account external influences during longitudinal studies; and how to confirm meaning in multi-language research. Finally, recommendations for future research include assessing the support required to maintain a patient safety focus during periods of major change or austerity; the skills needed by healthcare leaders; and the implications of poor data infrastructure.