Ovarian cancer patients in the UK are less likely to receive surgery than those in other developed countries, and this trial aims to fix that by giving doctors a digital tool to help them decide on the best treatment for each individual. The problem is unwarranted variation in care. Some patients are offered surgery, others are not, and the decision often depends more on the hospital or the doctor’s personal preference than on the patient’s actual condition. This project addresses that gap by combining a clinical decision-support algorithm with a structured shared decision-making process, so patients and clinicians can together choose the right treatment based on solid evidence. If the trial succeeds, the primary endpoint is a measurable increase in surgery rates across 18 UK treatment centres. The researchers hypothesise that better decision-making will reduce treatment variation and improve one-year survival. The abstract states that if every NHS region matched the current highest standard, 1,000 more women would live at least five years beyond diagnosis each year. The project also includes a cost-utility analysis to ensure the intervention is economically viable for the NHS, and a consolidation phase to embed the tool into routine practice and regulatory pathways.
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Background Outcomes for patients with ovarian cancer in the UK remain poor when compared to those in other developed countries. In part, this is related to unwarranted variation in the treatments offered to patients. The reasons behind this are complex but can be summarised as a paternalistic decision-making process and a lack of evidence to guide clinicians and patients. Here, we will build upon our previous work, in which we have developed decision-support algorithms, to carry out a comprehensive programme of work to improve outcomes for patients with this disease. Aims & Objectives We aim to establish the effectiveness of a complex multifaceted intervention consisting of digitally-delivered clinical decision support for ovarian cancer treatment based on individual patient characteristics, and a subsequent personalised shared decision-making process. We aim to show that, through improved quality of shared decision-making and decision satisfaction, there will be an increase in surgery rate overall across UK centres (primary endpoint), which we hypothesise will result in a reduction of unwarranted treatment variation, and improved one-year survival for patients with ovarian cancer. Experimental Plan The programme will take the form of four complementary work packages: WP1, Prepare. In this work package we will develop and test the resources needed for the intervention including the decision support tool and the information and training resources needed for shared decision-making. WP2, Study. IMPRESS2 is a phase 3 effectiveness-implementation hybrid clinical trial. It will take the form of a multicentre batched stepped-wedge cluster randomised trial using 18 UK based treatment centres and with a primary endpoint of rate of surgery and key secondary endpoints of decision satisfaction and overall survival. WP3 Evaluate. To assess the effectiveness of the intervention we will carry out a detailed cost-utility analysis. In addition we will carry out supplementary sub studies to explore views and experiences of patients and clinicians with the intervention WP4 Consolidate. Here we will focus on ensuring the legacy of the project by ensuring, firstly, fidelity of the intervention, secondly, that the intervention is developed in line with regulatory processes and, finally, that models for sustainability are developed. Impact The variation in treatment, and outcomes, for patients with ovarian cancer has been profound, long standing and worrying. Here we seek to demonstrate that this can be overcome by improving quality of decision-making, leading to reduction in treatment variation, and a subsequent improvement in overall survival. Improving survival in every NHS region to the current highest standard would mean 1,000 more women every year would live at least five years beyond their diagnosis.
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