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Title: Preoperative pathway errors in elective aortic surgery and the role of collaborative planning technology
Author: Godfrey, Anthony
ISNI:       0000 0004 7658 1454
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2018
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Healthcare services that are truly efficient, effective and safe offer the best experience and outcomes for patients and staff. However, healthcare is high risk and errors do occur. Consequently, safety in health care is now a priority for policymakers, healthcare organisations and service users worldwide. The overarching aim of this thesis is to gain a holistic appreciation of the elective preoperative pathway in aortic pathology, the errors that occur, whether they impact decision-making and intraoperative events, and to explore the role of a collaborative preoperative planning application (mPrePARE). Following an introduction to patient safety concepts (Chapter 1) we explore the background of error in aortic vascular practice (Chapter 2) before focusing on initiatives to improve safety in practice (Chapter 3). The empirical work of this thesis is then reported. An analysis of reported patient safety incidents through the National Reporting and Learning System is an example of contemporaneous big data analysis within the subspecialty of elective vascular aortic practice, providing insight into the landscape of error and harm, enhancing our focus of factors that impede quality care (Chapter 4). Building upon this we critically analyse the preoperative pathway and consider what may be considered 'Gold Standard' care (Chapter 5). We explore the preoperative pathway in an observational cross-sectional study (Chapter 6) and a longitudinal study of error occurrence from referral to treatment to examine whether these events truly impede decision making, case planning and intraoperative errors (Chapter 7). In a transition of safety concepts we explore the development, use and feasibility of a collaborative preoperative planning application in remedying previously identified errors, improving cross-MDT communication, efficiency and satisfaction with the process (Chapter 8). Chapters 9 and 10 provide a discussion of the work presented in this thesis, reflecting on the findings, implications for practice and future research considerations.
Supervisor: Bicknell, Colin ; Riga, Celia Sponsor: National Institute for Health Research ; Imperial College London
Qualification Name: Thesis (M.D.) Qualification Level: Doctoral