Evidence-Based Health Care Oxford University

Professor Karl Roberts, University of New England, NSW, Australia gives a talk on generative AI and large language models as applied to healthcare. Dr Karl Roberts is the Head of the School of Health and Professor of Health and Wellbeing at the University of New England, NSW, Australia. Karl has over thirty years-experience working in academia at institutions in Australia, the UK and USA. He has also acted as an advisor for various international bodies and governments on issues related to wellbeing, violence prevention and professional practice. Notably, this has included working with policing agencies, developing policy and practice on suicide, stalking, and homicide prevention. Interpol developing guidance for organisational responses to deliberate events such as biological weapon use. The UK government SAGE advisory group throughout the Covid19 pandemic focusing upon security planning. The European Union advising on biological terrorism, and extremist use of AI. World Health Organisation where he worked in a unit developing policy and practice related to deliberate biological threat events.

There has been substantial recent interest in the benefits and risks of artificial intelligence (AI). This has ranged from extolling its virtues as a harmless aid to decision making, as a tool in research, and as a means of improving economic productivity. To those claiming that unchecked AI is a significant threat to human wellbeing and could be an existential threat to humanity. One area of significant recent advancement in AI has been the field of Large Language Models (LLMs). Exemplified by tools such as Chat-GPT, or DALL-E, these so-called generative AI models allow individuals to generate new outputs through interacting with the models using simple natural language inputs. Various versions of LLMs have been applied to healthcare, and have variously been shown to be useful in areas as diverse as case formulation, diagnosis, novel drug discovery, and policy development. However, as with any new technology, there is a potential 'darkside,' and it is possible to utilise these tools for nefarious purposes. This talk will give a brief introduction to generative AI and large language models as applied to healthcare. It will then discuss the potential for misuse of these models, seeking to highlight how they may be misused and how significant a threat they could pose to health security. Finally we will consider strategies for managing the risks set against the possible benefits of generative AI. This talk is based on work carried out by the author and colleagues at the World Health Organisation and the Royal United Services Institute.

An introduction to OHA!, a tool currently being developed which aims to assist dentists in accessing the most reliable evidence regarding the effectiveness of common dental treatments. The OHA! repository has been purposefully crafted to be exceptionally selective and compact, ensuring that users can easily find straightforward and valuable answers to their dental clinical questions. During the presentation, Professor Paulo Nadanovsky will show two sample dental fact boxes (in draft form) that have been developed. One pertains to the success rate of root canal treatment, while the other focuses on the impact of shorter versus longer intervals between dental check-ups on oral health. Patients expect doctors to take action to help them, and the more treatment or diagnostic tests, the better they feel. Doctors genuinely want to help and can often come up with various treatment or testing options to try. Consequently, this leads to a situation where everyone becomes perpetual patients, regardless of their actual health needs and potential benefits – essentially, an overuse of healthcare. Furthermore, there is a pervasive illusion of certainty among healthcare professionals, including physicians and dentists. This illusion entails the belief that treatments are always effective, diagnostic tests are infallible, and there exists only a single, optimal treatment or management approach. Consequently, there is often a lack of systematic comparisons between the pros and cons of different options. To dispel this illusion of certainty, reduce excessive healthcare practices, and promote clear thinking when considering interventions, the provision of clear information is essential. The practice of evidence-based healthcare involves two distinct roles: that of evidence consumers and evidence producers. Consumers, including clinicians and the general public, often lack the expertise needed to evaluate and choose the most reliable evidence. Hence, it falls upon those producing healthcare evidence to assume the role of experts and develop tools that simplify the integration of the best available evidence into the decision-making process for clinicians and patients alike.

Bio: Professor Nadanovsky graduated as a dentist in Rio de Janeiro, Brazil. He worked for a few years in the clinic (mainly periodontics), then migrated to public health and epidemiology and obtained a PhD from the University of London in 1993. He witnessed the birth of the evidence-based medicine movement and of the Cochrane Collaboration while working as a lecturer at the London Hospital Medical College and at University College London (afterwards it was renamed Queen Mary and Westfield College). Professor Nadanovsky taught evidence-based dentistry between 1993 and 1997 and since 1997 has been teaching epidemiology and evidence-based health care to physicians, dentists, nutritionists, and other health care professionals. He supervises PhD and MSc students, and his main interest is in overdiagnosis and overtreatment in health care in general, and more specifically, in dentistry.

Using a variety of examples of fast and slow qualitative research this talk explores the affordances of rapid methods, and help researchers decide if and where to use them in their own work. Methodologies of rapid qualitative research have been around for decades, gaining particular prominence during the Covid-19 pandemic. They spark intense debate about the place of rapid qualitative methods in healthcare research. What questions are they best suited to answer? Is speed a trade-off for quality? Which parts of the qualitative process can and can't be done at pace? If can do research quicker, should we?

Dr Anna Dowrick (https://www.phc.ox.ac.uk/team/anna-dowrick) is an interdisciplinary social scientist, spanning medical sociology, medical anthropology and science and technology studies. Her research explores how social injustice can be seen and acted upon through understanding experiences of health and illness, with a view to informing and improving the design of public services. She has used rapid and slow qualitative designs to explore issues including: improving access to support for domestic violence and abuse, food poverty interventions, cancer detection, Covid and Long Covid, and beauty 'injectables'. Creative Commons Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Dr Sara Van Belle, Institute of Tropical Medicine, Antwerp gives a talk on the practice of realist inquiry in global health. Dr Sara Van Belle, Researcher at the Institute of Tropical Medicine, Antwerp provides an overview of the practice of realist inquiry origins and points of attraction or interest for global health systems and policy research. Specific challenges are discussed with some examples of applications in complex health programmes in LMIC and suggestions offered for further methodological development. Creative Commons Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Professor Seena Fazel, University of Oxford gives a talk on recent advances in prognostic modelling in psychiatry. Professor Seena Fazel, a Professor of Forensic Psychiatry at the University of Oxford gives a talk gives a talk on recent advances in prognostic modelling in psychiatry. A number that examine risk for adverse outcomes, such as self-harm suicide, and violent crime, have been developed in Oxford (OxRisk tools), and further research on their feasibility, useability and impact will be outlined. Methodological challenges in their development and validation will be discussed, and how impact can be tested. Creative Commons Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

Dr Derrick Bennett, University of Oxford gives a talk on the epidemiological evidence of alcohol and cardiovascular disease. Dr Derrick Bennett, Associate Professor at the Nuffield Department of Population Health, University of Oxford, presents an overview of the epidemiological evidence of alcohol and cardiovascular disease (CVD), describes how bias may have impacted on this observational evidence, and finally presents evidence for the causal relevance of alcohol for CVD disease based on MR studies.
Creative Commons Attribution-Non-Commercial-Share Alike 2.0 UK: England & Wales; http://creativecommons.org/licenses/by-nc-sa/2.0/uk/