PA-20-070 "Development of evidence-based decision support for the management of COVID19"

总结 优化COVID-19管理的关键是制定循证建议， 相关战略，以确保执行治疗建议。这一点尤其重要 当证据像COVID-19一样迅速出现时。建议分级 评估、发展和评价）已成为评估 建议的证据和力度。由110多个专业组织认可，GRADE 已经编纂了临床实践指南（CPG）小组应该考虑的关键规范因素， 考虑.在过去4年中，在我们的母公司R 01赠款（5 R 01 HS 024917）上进行的活动中，我们 我发现，除了规范性的GRADE因素，重要的非GRADE因素也会影响群体 CPG小组的判断。我们现在建议利用R 01补助金的这些发现， 为COVID-19制定最佳管理策略。提供最合理的框架， 管理COVID-19患者，我们建议制定基于GRADE的CPGs，我们将在 在芝加哥的拉什卫生系统内的护理点的电子病历（EMR）。一个日益 改善病人护理的流行策略是通过将CPG转化为临床路径来标准化护理 (CPs)，其通常使用流程图或临床算法来提供关于治疗过程的详细步骤。 管理特定的临床问题或整个护理范围。然而，尽管承诺 CP及其日益增加的使用，没有理论框架已经制定，以指导其发展。这 意味着不可能严格分析CPG/CP的效率，也不可能分析它们对患者健康的影响 成果。我们假设，发展CPG/CP的坚实理论基础可以由以下方面提供： 将其转换为快速和节俭的决策树（FFT）。FFT被构造为一系列顺序的- 有序的临床信息或“线索”，其关系由一系列if-then语句定义。每一个提示 FFT可以正确地或不正确地对信号进行分类（例如，患者患有COVID-19）与噪声（例如，病人 没有COVID-19）并且可以测量该分类模式（例如，信号是真阳性还是 阴性）。FFT的这一特性使其能够集成到更广泛的信号理论框架中。 检测和相关理论，这反过来又允许他们所代表的临床策略的准确性， 评估。在本申请中，我们建议为COVID-19（目标1）开发GRADE CPG， 转换为CP，并将CP转换为FFT。随后，我们将在Rush EMR中实现FFT（目标2）， 并进行中断的时间序列，以评估基于GRADE的FFT对管理 COVID-19患者建议的申请是直接通知的父R 01赠款，并已 对改善COVID-19患者的临床管理具有潜在的即时和持续影响。

SUMMARY A key to optimal management of COVID-19 is development of evidence-based recommendations and associated strategies to ensure implementation of treatment recommendations. This is particularly important when evidence is emerging as rapidly as is the case for COVID-19. GRADE (Grading of Recommendations Assessment, Development and Evaluation) has emerged as the leading system for rating the quality of evidence and strength of recommendations. Endorsed by more than 110 professional organizations, GRADE has codified key normative factors that clinical practice guidelines (CPGs) panels ought to take into consideration. During activities conducted over the last 4 years on our parent R01 grant (5R01HS024917), we have discovered that in addition to normative GRADE factors, important non-GRADE factors affect the group judgment of CPG panels. We now propose to leverage these findings from the parent R01 grant to help generate optimal management strategies for COVID-19. To provide the most rational framework for managing COVID-19 patients, we propose to develop GRADE-based CPGs that we will implement in the electronic medical record (EMR) at the point-of-care within the Rush health system in Chicago. An increasingly popular strategy for improving patient care is to standardize care by translating CPGs into clinical pathways (CPs), which typically use flow charts or clinical algorithms to provide detailed steps about a course of management for a particular clinical problem or an entire spectrum of care. However, despite the promise of CPs and their increasing use, no theoretical framework has been developed to guide their development. This means it is not possible to rigorously analyze the efficiency of CPGs/CPs, nor their influence on patient health outcomes. We hypothesize that solid theoretical grounds for developing CPGs/CPs can be provided by converting them into fast-and-frugal decision trees (FFTs). FFTs are constructed as a series of sequentially- ordered, clinical information or “cues” whose relation is defined by a series of if–then statements. Every cue in an FFT can correctly or incorrectly classify a signal (e.g., patient has COVID-19) vs. noise (e.g., patient does not have COVID-19) and this classification pattern can be measured (e.g., is the signal a true positive or negative). This property of FFTs allows them to be integrated within a broader theoretical framework of signal detection and related theories which, in turn, allows the accuracy of the clinical strategies they represent to be evaluated. In this application, we propose to develop GRADE CPGs for COVID-19 (Aim 1), translate the CPGs into CPs, and, convert the CPs into FFTs. Subsequently, we will implement FFTs in the Rush EMR (Aim 2), and conduct an interrupted time series to evaluate the effect of GRADE-based FFTs on management of patients with COVID-19. The proposed application is directly informed by the parent R01 grant and has potential for immediate and sustained impact to improve clinical management of patients with COVID-19.