Building local decision-making competencies during COVID-19: Accelerating the transition from learning healthcare systems to learning health communities

IF 2.6 Q2 HEALTH POLICY & SERVICES
Rohit Ramaswamy, Varun Ramaswamy, Margaret Holly, Sophia Bartels, Paul Barach
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引用次数: 3

Abstract

Introduction

The persisting and evolving COVID-19 pandemic has made apparent that no singular policy of mitigation at a regional, national or global level has achieved satisfactory and universally acceptable results. In the United States, carefully planned and executed pandemic policies have been neither effective nor popular and COVID-19 risk management decisions have been relegated to individual citizens and communities. In this paper, we argue that a more effective approach is to equip and strengthen community coalitions to become local learning health communities (LLHCs) that use data over time to make adaptive decisions that can optimize the equity and well-being in their communities.

Methods

We used data from the North Carolina (NC) county and zip code levels from May to August 2020 to demonstrate how a LLHC could use statistical process control (SPC) charts and simple statistical analysis to make local decisions about how to respond to COVID-19.

Results

We found many patterns of COVID-19 progression at the local (county and zip code) levels during the same time period within the state that were completely different from the aggregate NC state level data used for policy making.

Conclusions

Systematic approaches to learning from local data to support effective decisions have promise well beyond the current pandemic. These tools can help address other complex public health issues, and advance outcomes and equity. Building this capacity requires investment in data infrastructure and the strengthening of data competencies in community coalitions to better interpret data with limited need for advanced statistical expertise. Additional incentives that build trust, support data transparency, encourage truth-telling and promote meaningful teamwork are also critical. These must be carefully designed, contextually appropriate and multifaceted to motivate citizens to create and sustain an effective learning system that works for their communities.

Abstract Image

在2019冠状病毒病期间建立地方决策能力:加速从学习型卫生保健系统向学习型卫生社区的过渡
持续和不断演变的COVID-19大流行表明,在区域、国家或全球层面,没有任何单一的缓解政策能够取得令人满意和普遍接受的结果。在美国,精心规划和执行的大流行政策既不有效也不受欢迎,COVID-19风险管理决策已被移交给个人公民和社区。在本文中,我们认为,更有效的方法是装备和加强社区联盟,使其成为当地学习健康社区(llhc),这些社区利用数据随时间推移做出适应性决策,从而优化社区的公平和福祉。方法利用2020年5月至8月北卡罗来纳州县和邮政编码级别的数据,展示LLHC如何使用统计过程控制(SPC)图表和简单的统计分析来制定如何应对COVID-19的地方决策。结果我们发现,在该州同一时间段内,当地(县和邮政编码)层面的许多COVID-19进展模式与用于政策制定的北卡罗来纳州州级汇总数据完全不同。从当地数据中学习以支持有效决策的系统方法的前景远远超出了当前的大流行。这些工具可以帮助解决其他复杂的公共卫生问题,并促进成果和公平。建立这种能力需要对数据基础设施进行投资,并加强社区联盟的数据能力,以便在对高级统计专门知识的需求有限的情况下更好地解释数据。建立信任、支持数据透明度、鼓励说实话和促进有意义的团队合作的其他激励措施也至关重要。这些计划必须经过精心设计,适合具体情况,并从多方面激励公民创建和维持一个有效的学习系统,为他们的社区服务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Learning Health Systems
Learning Health Systems HEALTH POLICY & SERVICES-
CiteScore
5.60
自引率
22.60%
发文量
55
审稿时长
20 weeks
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