ROAD2H: Resource Optimisation, Argumentation, Decision Support and Knowledge Transfer to Create Value via Learning Health Systems

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).

The project will be carried out in three phases:
- Phase 1: To review existing HISs and infrastructures in three exemplar countries (China, Serbia, Myanmar), against the requirements for LHSs
- Phase 2: Iterative development of a prototype digital methodology (encompassing policy, data, cost-effectiveness analytics, optimization, argumentation, DSS and provenance) to be embedded into existing HISs
- Phase 3: To evaluate, by means of proof-of-concept prototype DSS tools in Serbia and China and by scoping in Myanmar, the technical and clinical feasibility of the ROAD2H methodology, its acceptability among end users, and its potential and preconditions for generalization across disease areas and health systems settings in order to support UHC.

As its primary purpose, ROAD2H has the potential to directly benefit 3 ODA countries, ranging low-income (Myanmar) to middle income (Serbia and China, with a focus on the latter's rural poor), through:
- reducing out of pocket healthcare spending and financial impoverishment,
- minimising households' precautionary saving thus boosting economic growth,
- reducing waste and improving efficiency of healthcare spending by governments and insurers, allowing fairer and more comprehensive coverage, contributing to the Sustainable Development Goal 3.8 on UHC.

As LMIC budgets grow, demands on those budgets grow too, both from expensive new healthcare technologies, an expanding base of citizens entitled to care through UHC and growing non-communicable disease burdens with growing wealth. Health ministries have limited capacity to control their investments or to make a case to treasuries for targeted investments in high priority diseases and populations. The resulting inefficiencies and inequities affect health as well as economic growth and political stability. ROAD2H will address these challenges by empowering payers with better information as to where their investment goes, and how such allocation compares to global but locally adapted norms for best practice. Our diverse sample of countries will enable us to adapt our methodologies to address the different challenges faced by LMICs: Serbia, a small European nation with a well-established HIS and billing system; China, a vast country with a relatively new HIS and a fragmented healthcare delivery model with low penetration of best practice; and Myanmar, one of the world's poorest countries, spending about 1% of its GDP on health but with major potential in leapfrogging developed economies through better use of digital technology.

There could be considerable impact from piloting ROAD2H methodologies among three regions of China (Xiamen City, Shaanxi, Henan) and Serbia, with a combined population of over 80m. There is also immense potential for scale-up across rural China (750m rural residents insured under the New China Medical Scheme), in addition for adapting and testing our methodologies in other ODA countries moving to UHC with nascent or established national health insurance schemes, where IGHI has strong links with senior policymakers (inc. India, Ghana, South Africa, Indonesia, Vietnam, Thailand) and where most of the world's poor reside. Our proposal is a much needed and timely contribution to developing countries' efforts towards the Sustainable Development Goal 3.8, aligning well with the strategic priorities of the Global Challenge Research Fund, namely 'sustainable health and wellbeing'.

Further, UN and multilateral organisations such as the World Bank recognise that Big Data will play an important role in monitoring countries' implementation of SDGs, however there currently exist few applications within health beyond infectious disease surveillance or m/eHealth platforms at the patient-clinician interface. We propose that to go much further by leveraging routinely collected data to optimise budget allocation and improve best practice at the system-wide level. This will help countries increase the efficiency and quality of their investment, improving patient referral and care integration across health system tiers, strengthen the generation and translation of locally-relevant knowledge, and ultimately realise UHC.

As a secondary objective, in addition to significant healthcare benefits in LMICs where the use cases will be developed and beyond, ROAD2H will ensure that the UK is at the forefront of the development of sophisticated knowledge and data management systems to advance healthcare provision and assist LMICs in enhancing their healthcare systems. ROAD2H will also strengthen foundations for UK-based commercial opportunities, expanding on our model of innovative public-private partnerships interfacing the healthcare, digital and creative sector