Next generation clinical guidance for primary care in South Africa – credible, consistent and pragmatic - Machingaidze, Shingai (PLoS One, 2018)
BACKGROUND: Agreed international development standards underpin high quality de novo clinical practice guidelines (CPGs). There is however, no international consensus on how high quality CPGs should ‘look’; or on whether high quality CPGs from one country can be viably implemented elsewhere. Writing de novo CPGs is generally resource-intensive and expensive, making this challenging in resource-poor environments. This paper proposes an alternative, efficient method of producing high quality CPGs in such circumstances, using existing CPGs layered by local knowledge, contexts and products. METHODS: We undertook a mixed methods case study in South African (SA) primary healthcare (PHC), building on findings from four independent studies. These comprised an overview of international CPG activities; a rapid literature review on international CPG development practices; critical appraisal of 16 purposively-sampled SA PHC CPGs; and additional interrogation of these CPGs regarding how, why and for whom, they had been produced, and how they ‘looked’. RESULTS: Despite a common aim to improve SA PHC healthcare practices, the included CPGs had different, unclear and inconsistent production processes, terminology and evidence presentation styles. None aligned with international quality standards. However many included innovative succinct guidance for end-users (which we classified as evidence-based summary recommendations, patient management tools or protocols). We developed a three-tiered model, a checklist and a glossary of common terms, for more efficient future production of better quality, contextually-relevant, locally-implementable SA PHC CPGs. Tier 1 involves transparent synthesis of existing high quality CPG recommendations; Tier 2 reflects local expertise to layer Tier 1 evidence with local contexts; and Tier 3 comprises tailored locally-relevant end-user guidance. CONCLUSION: Our model could be relevant for any resource-poor environment. It should reduce effort and costs in finding and synthesising available research evidence, whilst efficiently focusing scant resources on contextually-relevant evidence-based guidance, and implementation.