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Table 1 ‘Biases’ against patients and carers in traditional evidence-based medicine (EBM) and how they might be overcome

From: Six ‘biases’ against patients and carers in evidence-based medicine

Nature of bias

Impact on process of care

Impact on outcome

How this bias might be minimised

1. Most published research had minimal patient input

Example: evidence relates to options and outcome measures that patients themselves would not have chosen

The available menu of evidence-based choices reflects a biomedical framing and omits options that might be more acceptable and effective

Patient and public input to setting research priorities, study design, choice of outcome measures, and interpretation and dissemination of findings must be prioritised and effectively resourced

Recruitment methods to trials address only a fraction of the population

Study findings apply only to this sub-population

Diverse and questioning patient/carer steering group may help recruit more diverse and representative samples

2. EBM’s hierarchy of evidence tends to devalue the patient or carer experience

Abstracted evidence from population samples is given more weight than real, individual evidence from this patient/carer

The patient is effectively ‘regressed to the mean’ and offered the option(s) that the average patient would benefit most from

‘Personally significant evidence’ from a particular patient in the here and now should be systematically captured and treated as complementary to ‘statistically significant evidence’ from distant research populations

Qualitative evidence, even when robust and relevant, is rarely used to its full potential

Personalisation of care lacks nuance and context, because research addressing ‘how’, ‘why’, and ‘in what circumstances’ has not been used

Narrative, phenomenological, and ethnographic research designs should be viewed as complementary rather than inferior to epidemiological evidence – though qualitative, like quantitative, research must be appraised for rigour and relevance

3. EBM conflates patient-centredness with use of shared decision-making tools

The ‘patient’s agenda’ is framed through a medical lens and reduced to a series of decisions about tests and treatments

Humanistic aspects of the consultation (empathy, compassion, the therapeutic alliance) are devalued and may be overlooked

Working with humanities scholars and psychologists, EBM researchers should acknowledge and incorporate interdisciplinary approaches to extend and complement their current focus on shared decision-making

4. Power imbalances may suppress the patient’s voice

Much of the patient’s agenda will not get aired in the consultation

Advice that is given, and management plans that are ‘agreed’, may be ignored (but may be inappropriate anyway since they are based on a partial picture)

Working with social and political scientists, EBM researchers should collect and apply evidence on how to make consultations more democratic (see main text for examples)

5. EBM over-emphasises the clinical consultation

Clinicians underestimate the extent of self-management and the value of lay networks (in which people support and inform one another) both face-to-face and virtual

Clinicians and researchers focus on ‘interventions’ that they can deliver instead of considering how they can support models of care in which they are no longer central

Working with social scientists, EBM researchers should become comfortable with naturalistic designs for studying the patient in a real-world context and exploring the dynamics of social networks and online groups from a complex systems perspective

6. EBM is concerned mainly with people who seek (and can access) care

People with greatest need for evidence-based care are least likely to receive it

A ‘hidden denominator’ of hardest-to-reach sub-populations may remain undiscovered, hence EBM may appear to have solved more problems than it actually has

EBM researchers should embrace more explicitly a public health agenda, in which preferred study designs may be observational and developmental (including participatory co-design) rather than controlled experiments