Building to learn: Information technology innovations to enable rapid pragmatic evaluation in a learning health system

IF 2.6 Q2 HEALTH POLICY & SERVICES
Geetanjali Rajamani, Genevieve B. Melton, Deborah L. Pestka, Maya Peters, Iva Ninkovic, Elizabeth Lindemann, Timothy J. Beebe, Nathan Shippee, Bradley Benson, Abraham Jacob, Christopher Tignanelli, Nicholas E. Ingraham, Joseph S. Koopmeiners, Michael G. Usher
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引用次数: 0

Abstract

Background

Learning health systems (LHSs) iteratively generate evidence that can be implemented into practice to improve care and produce generalizable knowledge. Pragmatic clinical trials fit well within LHSs as they combine real-world data and experiences with a degree of methodological rigor which supports generalizability.

Objectives

We established a pragmatic clinical trial unit (“RapidEval”) to support the development of an LHS. To further advance the field of LHS, we sought to further characterize the role of health information technology (HIT), including innovative solutions and challenges that occur, to improve LHS project delivery.

Methods

During the period from December 2021 to February 2023, eight projects were selected out of 51 applications to the RapidEval program, of which five were implemented, one is currently in pilot testing, and two are in planning. We evaluated pre-study planning, implementation, analysis, and study closure approaches across all RapidEval initiatives to summarize approaches across studies and identify key innovations and learnings by gathering data from study investigators, quality staff, and IT staff, as well as RapidEval staff and leadership.

Implementation (Results)

Implementation approaches spanned a range of HIT capabilities including interruptive alerts, clinical decision support integrated into order systems, patient navigators, embedded micro-education, targeted outpatient hand-off documentation, and patient communication. Study approaches include pre-post with time-concordant controls (1), randomized stepped-wedge (1), cluster randomized across providers (1) and location (3), and simple patient level randomization (2).

Conclusions

Study selection, design, deployment, data collection, and analysis required close collaboration between data analysts, informaticists, and the RapidEval team.

Abstract Image

建设学习:在学习型卫生系统中实现快速务实评估的信息技术创新
学习型医疗系统(LHSs)可以反复生成可用于实践的证据,从而改善医疗服务并产生可推广的知识。实用临床试验非常适合 LHS,因为它们将真实世界的数据和经验与一定程度的方法论严谨性相结合,从而支持可推广性。为了进一步推动 LHS 领域的发展,我们试图进一步确定医疗信息技术 (HIT) 的作用,包括创新解决方案和出现的挑战,以改善 LHS 项目的交付。在 2021 年 12 月至 2023 年 2 月期间,我们从 51 份 RapidEval 计划申请中选出了 8 个项目,其中 5 个已经实施,1 个目前正在进行试点测试,2 个正在规划中。我们对所有 RapidEval 计划的研究前规划、实施、分析和研究结束方法进行了评估,通过收集研究调查人员、质量人员、IT 人员以及 RapidEval 工作人员和领导层的数据,总结了各项研究的方法,并确定了关键的创新和经验。实施方法涵盖一系列 HIT 功能,包括中断警报、集成到订单系统中的临床决策支持、患者导航、嵌入式微观教育、有针对性的门诊病人交接文档和患者沟通。研究方法包括预后与时间一致对照(1)、随机阶梯式对冲(1)、跨提供者(1)和地点(3)的群组随机化,以及简单的患者水平随机化(2)。研究的选择、设计、部署、数据收集和分析需要数据分析师、信息学家和 RapidEval 团队之间的密切合作。
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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