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本文引用的文献

1
Pragmatic (trial) informatics: a perspective from the NIH Health Care Systems Research Collaboratory.实用(试验)信息学:来自美国国立卫生研究院医疗保健系统研究协作实验室的视角
J Am Med Inform Assoc. 2017 Sep 1;24(5):996-1001. doi: 10.1093/jamia/ocx016.
2
Obama signs 21st Century Cures Act.奥巴马签署《21世纪治愈法案》。
Nat Biotechnol. 2017 Jan 10;35(1):6. doi: 10.1038/nbt0117-6a.
3
Real-World Evidence - What Is It and What Can It Tell Us?真实世界证据——它是什么以及能告诉我们什么?
N Engl J Med. 2016 Dec 8;375(23):2293-2297. doi: 10.1056/NEJMsb1609216.
4
Good Clinical Practice Guidance and Pragmatic Clinical Trials: Balancing the Best of Both Worlds.《良好临床实践指南与实用临床试验:兼顾二者之长》
Circulation. 2016 Mar 1;133(9):872-80. doi: 10.1161/CIRCULATIONAHA.115.019902.
5
The ADAPTABLE Trial and PCORnet: Shining Light on a New Research Paradigm.ADAPTABLE试验与PCORnet:照亮一种新的研究范式。
Ann Intern Med. 2015 Oct 20;163(8):635-6. doi: 10.7326/M15-1460. Epub 2015 Aug 25.
6
Design and implementation of a privacy preserving electronic health record linkage tool in Chicago.芝加哥一种隐私保护电子健康记录链接工具的设计与实现
J Am Med Inform Assoc. 2015 Sep;22(5):1072-80. doi: 10.1093/jamia/ocv038. Epub 2015 Jun 23.
7
Why we need community engagement in medical research.为什么我们需要社区参与医学研究。
J Investig Med. 2014 Aug;62(6):851-5. doi: 10.1097/JIM.0000000000000097.
8
Launching PCORnet, a national patient-centered clinical research network.启动 PCORnet,一个全国性的以患者为中心的临床研究网络。
J Am Med Inform Assoc. 2014 Jul-Aug;21(4):578-82. doi: 10.1136/amiajnl-2014-002747. Epub 2014 May 12.
9
CAPriCORN: Chicago Area Patient-Centered Outcomes Research Network.CAPriCORN:芝加哥地区以患者为中心的结局研究网络。
J Am Med Inform Assoc. 2014 Jul-Aug;21(4):607-11. doi: 10.1136/amiajnl-2014-002827. Epub 2014 May 12.
10
Effective recruitment strategies and community-based participatory research: community networks program centers' recruitment in cancer prevention studies.有效的招募策略与基于社区的参与性研究:社区网络项目中心在癌症预防研究中的招募工作
Cancer Epidemiol Biomarkers Prev. 2014 Mar;23(3):416-23. doi: 10.1158/1055-9965.EPI-13-0760.

一种新型患者招募策略:通过与临床数据关联直接从社区中选择患者。

A Novel Patient Recruitment Strategy: Patient Selection Directly from the Community through Linkage to Clinical Data.

作者信息

Zimmerman Lindsay P, Goel Satyender, Sathar Shazia, Gladfelter Charon E, Onate Alejandra, Kane Lindsey L, Sital Shelly, Phua Jasmin, Davis Paris, Margellos-Anast Helen, Meltzer David O, Polonsky Tamar S, Shah Raj C, Trick William E, Ahmad Faraz S, Kho Abel N

出版信息

Appl Clin Inform. 2018 Jan;9(1):114-121. doi: 10.1055/s-0038-1625964. Epub 2018 Feb 14.

DOI:10.1055/s-0038-1625964
PMID:29444537
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5843765/
Abstract

OBJECTIVE

This article presents and describes our methods in developing a novel strategy for recruitment of underrepresented, community-based participants, for pragmatic research studies leveraging routinely collected electronic health record (EHR) data.

METHODS

We designed a new approach for recruiting eligible patients from the community, while also leveraging affiliated health systems to extract clinical data for community participants. The strategy involves methods for data collection, linkage, and tracking. In this workflow, potential participants are identified in the community and surveyed regarding eligibility. These data are then encrypted and deidentified via a hashing algorithm for linkage of the community participant back to a record at a clinical site. The linkage allows for eligibility verification and automated follow-up. Longitudinal data are collected by querying the EHR data and surveying the community participant directly. We discuss this strategy within the context of two national research projects, a clinical trial and an observational cohort study.

CONCLUSION

The community-based recruitment strategy is a novel, low-touch, clinical trial enrollment method to engage a diverse set of participants. Direct outreach to community participants, while utilizing EHR data for clinical information and follow-up, allows for efficient recruitment and follow-up strategies. This new strategy for recruitment links data reported from community participants to clinical data in the EHR and allows for eligibility verification and automated follow-up. The workflow has the potential to improve recruitment efficiency and engage traditionally underrepresented individuals in research.

摘要

目的

本文介绍并描述了我们为开展务实性研究而制定的一种新颖策略,该策略用于招募代表性不足的社区参与者,并利用常规收集的电子健康记录(EHR)数据。

方法

我们设计了一种从社区招募合格患者的新方法,同时利用附属医疗系统为社区参与者提取临床数据。该策略涉及数据收集、关联和跟踪方法。在此工作流程中,在社区中识别潜在参与者并就其资格进行调查。然后通过哈希算法对这些数据进行加密和去识别,以便将社区参与者与临床站点的记录进行关联。这种关联允许进行资格验证和自动随访。通过查询EHR数据并直接对社区参与者进行调查来收集纵向数据。我们在两个国家研究项目(一项临床试验和一项观察性队列研究)的背景下讨论了这一策略。

结论

基于社区的招募策略是一种新颖的、低接触的临床试验入组方法,可吸引不同类型的参与者。在利用EHR数据获取临床信息和进行随访的同时,直接与社区参与者进行接触,能够实现高效的招募和随访策略。这种新的招募策略将社区参与者报告的数据与EHR中的临床数据相链接,允许进行资格验证和自动随访。该工作流程有可能提高招募效率,并使传统上代表性不足的个体参与到研究中来。