Transregional Study Highlighting the Increasing Burden of Urology Cancer Multidisciplinary Team Meetings Around the UK.

Introduction The urology multidisciplinary team meeting (MDT) is the key weekly meeting that allows the opportunity to review results and discuss management plans for all urological cancers within a department. As populations age and cancer detection and management improve, the demand for the MDT will increase. We conducted a collaborative transregional study within the UK to evaluate the current workload on the urology MDT. Methods The study was divided into two parts: a multicenter retrospective audit and a snapshot survey. Three UK hospitals in Birmingham, Liverpool, and Cardiff were recruited into the multicenter study. Each hospital provided full MDT lists for all weekly meetings between August 2017 and 2022. Retrospective data gathered included the number of patients discussed per week, the average age of patients per week, the time allocated to their weekly MDT, and the total number of consultants in the department. The second part of the study involved the distribution of an online questionnaire to urologists across the UK to obtain a snapshot picture with the above parameters. Results Snapshot data from 34 different UK hospitals showed MDT length ranged from 1-6 hours, patients discussed ranged from 10-90 per week, and the maximum average discussion time was 3.8 minutes per case. Furthermore, 76% (N = 28/37) of respondents said unnecessary cases were discussed. Varied suggestions were provided on how the MDT could be improved. Multicenter five-year data showed a rise in mean total numbers of patients discussed per week in all centers: a 34.8% increase in Birmingham (from 34.5 patients to 46.5 patients), a 23.5% increase in Liverpool (27.2 patients to 33.6 patients), and a 38.8% increase in Cardiff (22.7 patients to 31.5 patients). Hours per meeting were Birmingham (2), Liverpool (3), and Cardiff (4), which meant the average minutes per patient discussion were Birmingham (2.6), Liverpool (5.4), and Cardiff (7.6). Conclusion There is a rapidly rising trend across UK regions for the number of patients being discussed in the urology MDT meeting. The MDT structure and function across the country are highly variable. There is consensus that the MDT discusses cases that are unnecessary, and this has been recognized for many years. Widespread implementation of the latest MDT management guidelines is urgently required to ensure MDT meetings are able to function effectively and efficiently into the future.

The value of standards for health datasets in artificial intelligence-based applications.

Artificial intelligence as a medical device is increasingly being applied to healthcare for diagnosis, risk stratification and resource allocation. However, a growing body of evidence has highlighted the risk of algorithmic bias, which may perpetuate existing health inequity. This problem arises in part because of systemic inequalities in dataset curation, unequal opportunity to participate in research and inequalities of access. This study aims to explore existing standards, frameworks and best practices for ensuring adequate data diversity in health datasets. Exploring the body of existing literature and expert views is an important step towards the development of consensus-based guidelines. The study comprises two parts: a systematic review of existing standards, frameworks and best practices for healthcare datasets; and a survey and thematic analysis of stakeholder views of bias, health equity and best practices for artificial intelligence as a medical device. We found that the need for dataset diversity was well described in literature, and experts generally favored the development of a robust set of guidelines, but there were mixed views about how these could be implemented practically. The outputs of this study will be used to inform the development of standards for transparency of data diversity in health datasets (the STANDING Together initiative).

Exploring the experiences and views of doctors working with Artificial Intelligence in English healthcare; a qualitative study.

BACKGROUND: The National Health Service (NHS) aspires to be a world leader of Artificial Intelligence (AI) in healthcare, however, there are several barriers facing translation and implementation. A key enabler of AI within the NHS is the education and engagement of doctors, however evidence suggests that there is an overall lack of awareness of and engagement with AI. RESEARCH AIM: This qualitative study explores the experiences and views of doctor developers working with AI within the NHS exploring; their role within medical AI discourse, their views on the implementation of AI more widely and how they consider the engagement of doctors with AI technologies may increase in the future. METHODS: This study involved eleven semi-structured, one-to-one interviews conducted with doctors working with AI in English healthcare. Data was subjected to thematic analysis. RESULTS: The findings demonstrate that there is an unstructured pathway for doctors to enter the field of AI. The doctors described the various challenges they had experienced during their career, with many arising from the differing demands of operating in a commercial and technological environment. The perceived awareness and engagement among frontline doctors was low, with two prominent barriers being the hype surrounding AI and a lack of protected time. The engagement of doctors is vital for both the development and adoption of AI. CONCLUSIONS: AI offers big potential within the medical field but is still in its infancy. For the NHS to leverage the benefits of AI, it must educate and empower current and future doctors. This can be achieved through; informative education within the medical undergraduate curriculum, protecting time for current doctors to develop understanding and providing flexible opportunities for NHS doctors to explore this field.