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

ROAD 2 H将开发新的学习卫生系统（LHS）技术，以促进采取卫生干预措施，在低收入和中等收入国家实现全民健康覆盖（UHC）。全民健康覆盖是联合国可持续发展目标（SDG 3.8）中的一个具体目标，许多中低收入国家试图通过公共补贴的医疗保险计划（HIS）扩大优质医疗保健的人口覆盖面。两个问题是通过这些计划实现全民健康覆盖的关键：i）如何在相互竞争的临床干预措施中最佳分配预算（分配效率）ii）在什么时间和情况下，应在什么环境中为什么类型的患者提供特定的干预措施，以及达到什么样的护理标准（质量）。个别医疗干预措施的有效性分析，以及在整个疾病领域推荐最佳实践的循证指南/途径。然而，系统范围内的数学优化技术的使用有限（部分原因是大量的数据需求）;没有努力使用HIS常规收集的数据用于计费和索赔目的，以及电子健康记录（EHR），以提高效率和质量，尽管LMIC中现有的尝试将这些数据作为HTA和指南的输入进行回顾性使用。我们假设，国家HIS的LHS框架，结合资源优化，论证（如人工智能中所理解的），临床决策支持以及临床和政策分析，将促进采用基于证据，具有成本效益和数据驱动的医疗保健干预措施，并提高LMIC过渡到UHC的综合护理系统的整体效率和质量。ROAD 2 H将提供DSS工具，将个体患者数据（来自EHR）与本地化临床指南（包括HTA，临床路径）整合在一起，这些指南通过政策和临床分析从最佳临床和成本效益证据以及HIS账单/索赔数据中获得。我们将使用论证从成本效益和资源优化考虑中得出的参数化优化问题，然后使用数学优化中的标准方法来最大限度地提高临床干预的效率。我们还将使用论证来整合优化以实现最大效率，并将推理与本地化指南相结合，解决冲突并透明地解释临床干预的建议。DSS将利用这些解释的建议以及数据来源技术来生成和透明地收集患者定制的建议，以根据LHS方法进行进一步学习。DSS工具将在EHR/临床医生/患者交互中运行，并集成来自多个来源的数据，并具有本地化界面该项目将分三个阶段进行：第一阶段：审查三个示范国家现有的HIS和基础设施（中国、塞尔维亚、缅甸），对照LHS-第2阶段的要求：原型数字方法的迭代开发（包括政策、数据、成本效益分析、优化、论证、决策支持系统和来源）嵌入现有的HIS-第3阶段：通过塞尔维亚和中国的概念验证原型DSS工具以及缅甸的范围界定，评估ROAD 2 H方法的技术和临床可行性、最终用户的可接受性及其在疾病领域和卫生系统环境中推广的潜力和前提条件，以支持UHC。

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.