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World-class teaching and research that helps general practitioners and other health professionals deliver better care in the community.

  • Intuition and evidence - Uneasy bedfellows?

    6 April 2018

    Intuition is a decision-making method that is used unconsciously by experienced practitioners but is inaccessible to the novice. It is rapid, subtle, contextual, and does not follow simple, cause-and-effect logic. Evidence-based medicine offers exciting opportunities for improving patient outcomes, but the 'evidence-burdened' approach of the inexperienced, protocol-driven clinician is well documented. Intuition is not unscientific. It is a highly creative process, fundamental to hypothesis generation in science. The experienced practitioner should generate and follow clinical hunches as well as (not instead of) applying the deductive principles of evidence-based medicine. The educational research literature suggests that we can improve our intuitive powers through systematic critical reflection about intuitive judgements - for example, through creative writing and dialogue with professional colleagues. It is time to revive and celebrate clinical storytelling as a method for professional education and development. The stage is surely set for a new, improved - and, indeed, evidence-based - 'Balint' group.

  • Happy forms?

    1 December 2017

  • Computer templates in chronic disease management: Ethnographic case study in general practice

    28 January 2018

    Objective: To investigate how electronic templates shape, enable and constrain consultations about chronic diseases. Design: Ethnographic case study, combining field notes, video-recording, screen capture with a microanalysis of talk, body language and data entry - an approach called linguistic ethnography. Setting: Two general practices in England. Participants and methods: Ethnographic observation of administrative areas and 36 nurse-led consultations was done. Twenty-four consultations were directly observed and 12 consultations were video-recorded alongside computer screen capture. Consultations were transcribed using conversation analysis conventions, with notes on body language and the electronic record. The analysis involved repeated rounds of viewing video, annotating field notes, transcription and microanalysis to identify themes. The data was interpreted using discourse analysis, with attention to the sociotechnical theory. Results: Consultations centred explicitly or implicitly on evidence-based protocols inscribed in templates. Templates did not simply identify tasks for completion, but contributed to defining what chronic diseases were, how care was being delivered and what it meant to be a patient or professional in this context. Patients' stories morphed into data bytes; the particular became generalised; the complex was made discrete, simple and manageable; and uncertainty became categorised and contained. Many consultations resembled bureaucratic encounters, primarily oriented to completing data fields. We identified a tension, sharpened by the template, between different framings of the patient - as 'individual ' or as 'one of a population' . Some clinicians overcame this tension, responding creatively to prompts within a dialogue constructed around the patient's narrative. Conclusions: Despite their widespread implementation, little previous research has examined how templates are actually used in practice. Templates do not simply document the tasks of chronic disease management but profoundly change the nature of this work. Designed to assure standards of 'quality' care they contribute to bureaucratisation of care and may marginalise aspects of quality care which lie beyond their focus. Creative work is required to avoid privileging 'institution-centred' care over patient-centred care.

  • Selling drugs

    1 December 2017

  • Risk models and scores for type 2 diabetes: Systematic review

    23 March 2018

    Objective: To evaluate current risk models and scores for type 2 diabetes and inform selection and implementation of these in practice. Design: Systematic review using standard (quantitative) and realist (mainly qualitative) methodology. Inclusion criteria: Papers in any language describing the development or external va lidation, or both, of models and scores to predict the risk of an adult developing type 2 diabetes. Data sources: Medline, PreMedline, Embase, and Cochrane databases were searched. Included studies were citation tracked in Google Scholar to identify follow-on studies of usability or impact. Data extraction: Data were extracted on statistical properties of models, details of internal or external validation, and use of risk scores beyond the studies that developed them. Quantitative data were tabulated to compare model components and statistical properties. Qualitative data were analysed thematically to identify mechanisms by which use of the risk model or score might improve patient outcomes. Results: 8864 titles were scanned, 115 full text papers considered, and 43 papers included in the final sample. These described the prospective development or validation, or both, of 145 risk prediction models and scores, 94 of which were studied in detail here. They had been tested on 6.88 million participants followed for up to 28 years. Heterogeneity of primary studies precluded meta-analysis. Some but not all risk models or scores had robust statistical properties (for example, good discrimination and calibration) and had been externally validated on a different population. Genetic markers added nothing to models over clinical and sociodemographic factors. Most authors described their score as "simple" or "easily implemented," although few were specific about the intended users and under what circumstances. Ten mechanisms were identified by which measuring diabetes risk might improve outcomes. Follow-on studies that applied a risk score as part of an intervention aimed at reducing actual risk in people were sparse. Conclusion: Much work has been done to develop diabetes risk models and scores, but most are rarely used because they require tests not routinely available or they were developed without a specific user or clear use in mind. Encouragingly, recent research has begun to tackle usability and the impact of diabetes risk scores. Two promising areas for further research are interventions that prompt lay people to check their own diabetes risk and use of risk scores on population datasets to identify high risk "hotspots" for targeted public health interventions.