Getting Evidence into Practice

In the early days of CLAHRCs, ‘getting evidence into practice‘ was an important objective. We set about closing the T2 gap and used implementation science to get doctors to prescribe evidence-based care, dentists to use tooth protecting resins, and nurses to make regular observations. That is to say, we were concerned with how to make practitioners comply with standards over which they had complete jurisdiction. Theories of individual behaviour change were invoked, and rather then choose a theory on the basis of its impressive sounding title (e.g. prospect theory, social network theory), a framework was developed to identify barriers and facilitators of change.[1]

But practitioners increasingly follow the evidence when it is compelling and when the evidence-based standard is in their gift.[2] So, the big (and much more interesting) problem now is how to change the service in a generic way rather than simply to increase performance on a specific measure – we are becoming more concerned with draining the swamp than zapping individual mosquitoes.[3] In our CLAHRC we recently evaluated a compound (multi-component) intervention to improve home dialysis rates, having promulgated a guideline supporting improved access to such a service. We showed that agreement with the proposed change among stakeholders, an agreed

Implementation plan, managerial support, and product champions all facilitated the success of the intervention in taking West Midlands from the worst to the best performing region in England. However, the king of all intervention components was a financial incentive.[4] Fulop and colleagues have now published a similar multi-methods evaluation of an arguably even more complex intervention to improve access to acute stroke care.[5] The findings are very similar, save that we found more emphasis on financial incentives and also more problems in communication with patients; something that would perhaps not stand out in the hyper acute stroke context. The Fulop paper is an advance on ours in (at least) two respects. First, they compare and contrast across two regions/CLAHRCs and I always think controls should be used if possible; even one is better than none. Second, they illustrate the causal model with diagrams that make the theoretical framework they are using clear, a practice that is helpful in communicating the very real distinctions between the intervention as planned, its implementation/adaption, its upstream effects (e.g. staff knowledge/morale), its downstream effects (at the patient ‘level’), and the context in which all takes place.[6] People muddle these concepts and hence fall over their feet , but Fulop and colleagues have shown themselves to be sure-footed!

— Richard Lilford,

References:

  1. Michie S, van Stralen M, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventionsImplement Sci. 2011; 6: 42.
  2. Johnson N, Sutton J, Thornton JG, Lilford RJ, Johnson VA, Peel KR. Decision analysis for best management of mildly dyskaryotic smear. Lancet. 1993;342(8863):91-6
  3. Lilford RJ, Chilton PJ, Hemming K, Girling AJ, Taylor CA, Barach P. Evaluating policy and service interventions: framework to guide selection and interpretation of study end pointsBMJ. 2010; 341: c4413.
  4. Combes G, Allen K, Sein K, Girling A, Lilford R. Taking hospital treatments home: a mixed methods case study looking at the barriers and success factors for home dialysis treatment and the influence of a target on uptake ratesImplement Sci. 2015; 10: 148.
  5. Fulop NJ, Ramsay AIG, Perry C, et al. Explaining outcomes in major system change: a qualitative study of implementing centralised acute stroke services in two large metropolitan regions in England. Implement Sci. 2016; 11: 80.
  6. Lilford RJ, Chilton PJ, Hemming K, Girling AJ, Taylor CA, Barach P. Evaluating policy and service interventions: framework to guide selection and interpretation of study end points. BMJ. 2010; 341: c4413.
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