Producing the BEANs needed for person-centred healthcare decision making requires translating the wisdom of the clinical crowd

Producing the BEANs needed for person-centred healthcare decision making requires translating the wisdom of the clinical crowd Mette Kjer Kaltoft, University of Southern Denmark Øystein Eiring, Norwegian Knowledge Centre for the Health Services Jesper Bo Nielsen, University of Southern Denmark Glenn Salkeld, University of Sydney School of Public Health Jack Dowie, London School of Hygiene and Tropical Medicine (presenting) Abstract (500) Person-centred care is the increasingly avowed aim of health services and professionals. To be meaningful such care requires a shared decision making process in which an individual's preferences over the multiple criteria that matter to them are synthesised with the Best Estimate Available Now (at the point of decision) for how well each of the available options will perform on each criterion. Conventional evidence-based approaches can meet the latter requirement in relation to the BEANs only by assuming professionals are able to make up the shortfalls remaining after the peer-reviewed published products of scientific research have been fully exploited. Since the clinical judgement of individual professionals has never been subjected to scientific validation in this respect, we have a situation where demonstrated scientific rigour is simultaneously regarded as essential and irrelevant to clinical decision making. Attempts to increase the external validity of scientific studies (notably randomised controlled trials) are attractive to many, but can never get near to meeting the needs of practice. What is required is the translation of the wisdom of the clinical crowd through the processing of the beliefs of expert professionals into BEANs. The process must be appropriately analytically rigorous, but this should not be confounded with scientific rigour. A binary concept of ‘evidence’, as that which meets or doesn't meet some general, value judgement-based threshold (e.g. p<.05), is inappropriate for individual decision makers, who have their own trade-offs and error loss functions. It creates much of the 'know-do' gap and perceived ‘translation’ problem at the final ‘bedside’ stage. However, given the variations in clinician’s beliefs and limited statistical competencies (as established by Gigerenzer and colleagues), their beliefs require systematic processing. Needed is the reformulation of the research community's task as being the continuous (‘living’) production of BEANs, within a Bayesian framework and by a process that is ARAPAN - As Rigorous As Practical And Necessary. This will involve the systematic elicitation and analysis of the potential ‘big data’ that expert beliefs collectively represent, as well as the exploitation of observational studies and big data included in health records and elsewhere. To give substance to this argument we inserted the results from a recent high-quality Mixed Treatment Comparison (Network Meta-Analysis) on medications for Generalised Anxiety Disorder into a Multi-Criteria Decision Analysis (link to online version). The criteria included (Response, Remission, and Tolerability) were determined by data availability in trials and are not those that would be produced by a survey of person-as-patient important outcomes. Minimally the latter would distinguish different types of side effects and adverse events, given the known existence of, and differential concern with, effects on sexual, and other functions. Working to the standards appropriate to practice as opposed to science, and simultaneously increasing the range of source inputs to include expert beliefs, is essential to give such modelling research practical relevance for person-centred care. Major benefits in the form of establishing priorities for person-centred research will follow by way of ‘backward translation’ of the need for better BEANs for many person-as-patient important outcomes.