Completed Public Health & Healthcare Pregnancy, Children & Inherited Conditions

The Peek Practice-based Evidence Framework

In plain English

AI plain-English summary

Eye care services in low- and middle-income countries are stuck with slow, outdated methods for testing improvements—taking years to produce results that often arrive too late to be useful.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.

View original technical description
Health system strengthening, as endorsed by the WHO’s global action plan for universal eye health, facilitated a 25% reduction in global visual impairment between 1990 and 2015. New mobile health technologies have potential to greatly enhance systems strengthening, driving further revolutionary improvements in eye care. Currently, lack of detail in health system data and delayed information flows results in sporadic health systems’ improvement rarely founded on locally derived evidence. Testing single changes typically requires classical research studies, often taking years to produce results, by which time the environment may have changed, causing study data not informing change in practice. In the commercial software sector, a variety of testing methodologies are employed to develop and optimise web-based systems. Learning from these, we propose developing a hypothesis-driven, agile health service improvement approach enabling responsive changes to eye care service systems (our specific field) in low- and middle-income countries. This will use techniques including A/B testing and data-driven perpetual optimisation. These hypothesis-driven methodologies will allow quicker testing, analysis and implementation of service improvements with greater potential for translation to other settings. Used by local health system leaders, the tools could lead to a paradigm change in health system strengthening, improving health for millions.

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Researchers

Michael Gichangi (EPMC Awardee)Nigel Bolster (EPMC Awardee)Oathokwa Nkomazana (EPMC Awardee)

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Original classification

Collaborative Award in Science

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