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Nat Med. 2019 Jan;25(1):2-5. doi: 10.1038/s41591-018-0314-1.
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A real-time screening alert improves patient recruitment efficiency.实时筛查警报可提高患者招募效率。
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Effect of a clinical trial alert system on physician participation in trial recruitment.一项临床试验警报系统对医生参与试验招募的影响。
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Using technology to address recruitment issues in the clinical trial process.利用技术解决临床试验过程中的招募问题。
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JITA:一个实现即时医疗点患者招募的平台。

JITA: A Platform for Enabling Real Time Point-of-Care Patient Recruitment.

作者信息

Lee Vincent, Parekh Ketan, Matthew George, Shi Qiming, Pelletier Keith, Canale Aneth, Luzuriaga Katherine, Mathew Jomol

机构信息

University of Massachusetts Medical School, Worcester, MA 01655, USA.

出版信息

AMIA Jt Summits Transl Sci Proc. 2020 May 30;2020:355-359. eCollection 2020.

PMID:32477655
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7233033/
Abstract

Timely accrual continues to be a challenge in clinical trials. The evolution of Electronic Health Record systems and cohort selection tools like i2b2 have improved identification of potential candidate participants. However, delays in receiving relevant patient information and lack of real time patient identification cause difficulty in meeting recruitment targets. The authors have designed and developed a proof of concept platform that informs authorized study team members about potential participant matches while the patient is at a healthcare setting. This Just-In-Time Alert (JITA) application leverages Health Level 7 (HL7) messages and parses them against study eligibility criteria using Amazon Web Services (AWS) cloud technologies. When required conditions are satisfied, the rules engine triggers an alert to the study team. Our pilot tests using difficult to recruit trials currently underway at the UMass Medical School have shown significant potential by generating more than 90 patient alerts in a 90-day testing timeframe.

摘要

在临床试验中,及时招募受试者仍然是一项挑战。电子健康记录系统和诸如i2b2之类的队列选择工具的发展,改善了对潜在候选参与者的识别。然而,接收相关患者信息的延迟以及缺乏实时患者识别,导致难以实现招募目标。作者设计并开发了一个概念验证平台,当患者处于医疗机构时,该平台会向授权的研究团队成员告知潜在的参与者匹配情况。这种即时警报(JITA)应用程序利用健康级别7(HL7)消息,并使用亚马逊网络服务(AWS)云技术根据研究纳入标准对其进行解析。当满足所需条件时,规则引擎会向研究团队触发警报。我们在马萨诸塞大学医学院目前正在进行的难以招募受试者的试验中进行的试点测试表明,在90天的测试期内产生了90多个患者警报,显示出了巨大的潜力。