ROAD2H will develop novel Learning Health System (LHS) techniques to facilitate uptake of health interventions to achieve Universal Health Coverage (UHC) in Low- and Middle-Income Countries (LMICs). UHC is a specific target in the UN Sustainable Development Goals (SDG 3.8), with many LMICs attempting to extend population coverage of quality healthcare via public-subsidised health insurance schemes (HISs). Two issues are key to achieving UHC via these schemes: i) how budgets should be best allocated among competing clinical interventions (allocative efficiency) ii) under what timing and circumstances particular interventions should be offered for what kinds of patients in which settings and to what standard of care (quality).Current attempts in LMICs to address efficiency and quality issues include Health Technology Assessment (HTA) based on cost-effectiveness analysis of individual healthcare interventions, and evidence-based guidelines/pathways recommending best practice within whole disease areas. However there have been limited uptake of system-wide mathematical optimisation techniques (in part due to significant data requirements); no efforts to using data routinely collected by HISs for billing and claims purposes, as well as Electronic Health Records (EHRs), for improving efficiency and quality, even though existing attempts among LMICs to use such data retrospectively as inputs into HTA and guidelines show promise.
We hypothesise that a LHS framework for national HISs, combining resource optimization, argumentation (as understood in AI), clinical decision support and clinical and policy analytics, will facilitate the uptake of evidence-based, cost-effective and data-driven healthcare interventions, and improve the overall efficiency and quality of integrated care systems in LMICs transitioning to UHC.
ROAD2H will deliver DSS tools that integrate individual patient data (drawn from EHRs) with localized clinical guidelines (including HTA, clinical pathways) obtained via policy and clinical analytics from best available clinical and cost-effectiveness evidence as well as HIS billing/claim data. We will use argumentation to parameterize optimisation problems drawn from cost-effectiveness and resource optimization considerations, and then use standard methods in mathematical optimisation to maximize efficiency of clinical interventions. We will use argumentation also to integrate optimization for maximal efficiency and reasoning with localized guidelines, and resolve conflicts as well as transparently explain recommendations for clinical interventions. The DSS will make use of these explained recommendations as well as data provenance techniques to generate and transparently collect patient-tailored recommendations to enable further learning, in line with the LHS methodology. The DSS tools will operate at the EHR/clinician/patient interaction and integrate data from multiple sources as well as have localised interfaces (by country and possibly hospital/clinician).