Improving Performance of a Pediatric TeleMedicine and Medication Delivery Service through mHealth Technology

项目摘要/摘要 急性呼吸道感染和腹泻是导致儿童死亡的两个主要原因 在全球范围内只有一个月和五岁。这些常见的问题有低成本的治疗方法，但这些治疗方法 如果及早给药最有效。在资源有限的情况下，这是困难的，特别是在晚上。 基于NIH资助的5年多的形成性实施科学研究，我们的团队已经建立和 部署了名为MotoMeds的远程医疗和药物递送服务(TMDS)，以缩短夜间时间 获得照顾儿童的机会。其基本原理是TMDS产生乘数效应，以降低死亡率和 与仅提供面对面的紧急医疗服务(EMS)相比，发病率更高。一个 TMD与EMS的不同之处在于，它从一个集中的位置动员资源，并将这些资源 将资源分配给家庭。另一方面，EMS识别家庭中的患者并将 将患者集中到资源集中。EMS物流、临床指南和决策支持工具不是很容易 适用于TMDS。需要新的方法来确保TMDS医疗保健提供模式安全到达 他们的潜力。MotoMeds于2019年在海地推出，作为专注于安全和物流的预试 可行性，并在2021年的试点研究中进行了规模配置。我们开发了基于纸张的决策支持工具 适用于源自世界卫生组织(WHO)指导方针的虚拟呼叫中心检查。 我们的研究揭示了对电子临床决策支持(ECDS)以确保指南的迫切需求 规模化坚持，需要信心十足地分配分诊级别，尽管虚拟远程医疗存在限制 环境。准确的分诊对于确定TMDS护理路径至关重要：单独分娩(轻度病例)， 药物递送加上面对面检查(中等病例)和EMS/医院转诊(严重病例)。在 R21，我们将通过同时1)通过以下方式设计ECDS工具的阿尔法原型来满足这些需求 以人为本的设计，然后进行定性研究分析，同时2)使用我们之前的现有数据 两项研究以推导和内部验证用于集成的疾病严重性预测(DSP)算法 进入ECDS工具。在R33中，我们将从外部验证DSP的准确性，并评估 在试点阶梯式楔形集群随机试验中的测试版ECDS原型的性能。这个 测试版ECDS原型的性能将通过比较遵守指南的比率来确定 (主要结果衡量标准)和供应商之间使用纸质(对照)和电子产品的后勤指标 (干预)决策支持。这一单站点试点将为未来提供基本的经验和指标 电子捕获与数字信号处理器结合的多点随机对照试验。

PROJECT SUMMARY / ABSTRACT Acute respiratory infection and diarrheal disease are the two leading causes of pediatric death between 1 month and 5 years of age globally. These common problems have low-cost treatments, but these treatments are most effective when administered early. This is difficult in resource-limited settings, especially at night. Based on over 5 years of formative NIH-funded implementation science research, our team has built and deployed a telemedicine and medication delivery service (TMDS) called MotoMeds to improve nighttime access to care for children. The rationale is that a TMDS generates a multiplier effect to reduce mortality and morbidity to a greater extent than the provision of in-person emergency medical services (EMS) alone. A TMDS differs from EMS in that it mobilizes resources from a centralized location and transports these resources to households. An EMS, on the other hand, identifies patients at households and transports the patients to centralized resources. EMS logistics, clinical guidelines and decision-support tools do not readily apply to a TMDS. Novel methods are needed to assure that TMDS models of healthcare delivery safely reach their potential. MotoMeds was launched in Haiti in 2019 as a pre-pilot focused on safety and logistical feasibility and configured for scale in a pilot study in 2021. We developed paper-based decision-support tools for virtual call center exams that were derived from in-person World Health Organization (WHO) guidelines. Our research exposed a critical need for electronic clinical decision support (eCDS) to assure guideline adherence at scale and a need to confidently assign triage levels despite limitations in virtual telemedicine environments. Accurate triage is essential to determine TMDS pathways of care: delivery alone (mild cases), medication delivery plus an in-person exam (moderate cases) and EMS/hospital referral (severe cases). In the R21, we will address these needs by concurrently 1) designing an alpha prototype of the eCDS tool through human centered design followed by qualitative research analysis, while 2) using existing data from our prior two studies to derive and internally validate a disease severity prediction (DSP) algorithm for integration into the eCDS tool. In the R33, we will externally validate the accuracy of the DSP and evaluate the performance of a beta eCDS prototype in a pilot stepped wedge cluster randomized trial. The performance of the beta eCDS prototype will be determined by comparing rates of guideline adherence (primary outcome measure) and logistical metrics among providers using paper (control) vs electronic (intervention) decision support. This single-site pilot will provide essential experience and metrics for a future multi-site randomized controlled trial of the eCDS integrated with the DSP.