阿尔法新南威尔士州:如何才能创建一个全州范围的儿科全民学习保健系统?

Michael Hodgins, Nora Samir, Susan Woolfenden, Nan Hu, Francisco Schneuer, Natasha Nassar, Raghu Lingam
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摘要

背景:儿童在最初 2000 天内的健康和福祉对其教育成就和日后长期慢性疾病有着持久的影响。然而,高质量的数据、分析能力和及时的健康改善措施之间缺乏整合,这意味着从业人员、服务领导者和政策制定者无法有效地利用数据来规划和评估早期干预服务以及监测高层次的健康结果:我们的探索性研究旨在深入了解全州儿科学习型医疗系统(LHS)的系统和临床要求,该系统利用日常收集的数据,不仅可以确定护理中的不公平和差异所在,还可以为最需要的服务开发和提供提供信息:我们的方法包括:回顾澳大利亚如何使用行政数据的范例;咨询临床、政策和数据方面的利益相关者,以确定他们对儿童健康本地保健系统的需求;绘制儿童生命最初 2000 天内收集的现有数据点,并在地理空间上确定儿童健康需求关键指标的模式:结果:我们的研究确定了可用和可获取的指标,为提供服务提供了依据,并展示了利用日常收集的行政数据来确定健康需求与服务可用性之间差距的潜力:我们建议改进数据收集、获取和整合工作,以建立全州范围的 LHS,从而简化数据清理、分析和可视化流程,帮助及时发现需要帮助的人群。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Alpha NSW: What would it take to create a state-wide paediatric population-level learning health system?

Background: The health and well-being of children in the first 2000 days has a lasting effect on educational achievement and long-term chronic disease in later life. However, the lack of integration between high-quality data, analytic capacity and timely health improvement initiatives means practitioners, service leaders and policymakers cannot use data effectively to plan and evaluate early intervention services and monitor high-level health outcomes.

Objective: Our exploratory study aimed to develop an in-depth understanding of the system and clinical requirements of a state-wide paediatric learning health system (LHS) that uses routinely collected data to not only identify where the inequities and variation in care are, but also to also inform service development and delivery where it is needed most.

Method: Our approach included reviewing exemplars of how administrative data are used in Australia; consulting with clinical, policy and data stakeholders to determine their needs for a child health LHS; mapping the existing data points collected across the first 2000 days of a child's life and geospatially locating patterns of key indicators for child health needs.

Results: Our study identified the indicators that are available and accessible to inform service delivery and demonstrated the potential of using routinely collected administrative data to identify the gap between health needs and service availability.

Conclusion: We recommend improving data collection, accessibility and integration to establish a state-wide LHS, whereby there is a streamlined process for data cleaning, analysis and visualisation to help identify populations in need in a timely manner.

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