The Peek Practice-based Evidence Framework
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AI plain-English summaryEye care services in low- and middle-income countries are stuck with slow, outdated methods for testing improvements—taking years to produce results that often arrive too late to be useful. The researchers propose borrowing a technique from the commercial software world: A/B testing, where two versions of a system run simultaneously to see which performs better, combined with data-driven perpetual optimisation. This matters because current health system data is too vague and too slow to guide local decision-making. Without rapid feedback, clinics and hospitals cannot adapt to changing conditions or test small changes efficiently. The WHO’s global action plan for universal eye health helped cut visual impairment by 25% between 1990 and 2015, but further progress requires faster, locally grounded evidence. If successful, this framework would let local health system leaders test, analyse, and implement service improvements in weeks rather than years. The approach could shift how entire health systems are strengthened, not just in eye care but potentially across other services in low-resource settings. The tools are designed for use by local teams, not external researchers, making improvements more sustainable and scalable. For millions of people in low- and middle-income countries, this could mean better access to effective eye care without waiting decades for change.
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