Suppr超能文献

电子患者招募系统的用户需求:在 3 家德国大学医院首次实施后的半结构化访谈分析。

User Requirements for an Electronic Patient Recruitment System: Semistructured Interview Analysis After First Implementation in 3 German University Hospitals.

机构信息

Institute for Community Medicine, Section Epidemiology of Health Care and Community Health, University Medicine Greifswald, Greifswald, Germany.

Institute of Medical Informatics, Justus Liebig University, Giessen, Germany.

出版信息

JMIR Hum Factors. 2024 Sep 27;11:e56872. doi: 10.2196/56872.

Abstract

BACKGROUND

Clinical trials are essential for medical research and medical progress. Nevertheless, trials often fail to reach their recruitment goals. Patient recruitment systems aim to support clinical trials by providing an automated search for eligible patients in the databases of health care institutions like university hospitals. To integrate patient recruitment systems into existing workflows, previous works have assessed user requirements for these tools. In this study, we tested patient recruitment systems KAS+ and recruIT as part of the MIRACUM (Medical Informatics in Research and Care in University Medicine) project.

OBJECTIVE

Our goal was to investigate whether and to what extent the 2 different evaluated tools can meet the requirements resulting from the first requirements analysis, which was performed in 2018-2019. A user survey was conducted to determine whether the tools are usable in practice and helpful for the trial staff. Furthermore, we investigated whether the test phase revealed further requirements for recruitment tools that were not considered in the first place.

METHODS

We performed semistructured interviews with 10 participants in 3 German university hospitals who used the patient recruitment tools KAS+ or recruIT for at least 1 month with currently recruiting trials. Thereafter, the interviews were transcribed and analyzed by Meyring method. The identified statements of the interviewees were categorized into 5 groups of requirements and sorted by their frequency.

RESULTS

The evaluated recruIT and KAS+ tools fulfilled 7 and 11 requirements of the 12 previously identified requirements, respectively. The interviewed participants mentioned the need for different notification schedules, integration into their workflow, different patient characteristics, and pseudonymized screening lists. This resulted in a list of new requirements for the implementation or enhancement of patient recruitment systems.

CONCLUSIONS

Trial staff report a huge need of support for the identification of eligible trial participants. Moreover, the workflows in patient recruitment differ across trials. For better suitability of the recruitment systems in the workflow of different kinds of trials, we recommend the implementation of an adjustable notification schedule for screening lists, a detailed workflow analysis, broad patient filtering options, and the display of all information needed to identify the persons on the list. Despite criticisms, all participants confirmed to use the patient recruitment systems again.

摘要

背景

临床试验对于医学研究和医学进步至关重要。然而,临床试验往往无法达到其招募目标。患者招募系统旨在通过在大学医院等医疗机构的数据库中为合格患者提供自动搜索来支持临床试验。为了将患者招募系统集成到现有工作流程中,之前的工作已经评估了这些工具的用户需求。在这项研究中,我们测试了 KAS+和 recruIT 作为 MIRACUM(大学医学中的研究和护理中的医学信息学)项目的一部分的患者招募系统。

目的

我们的目标是调查这两种不同的评估工具是否以及在何种程度上能够满足 2018-2019 年进行的第一次需求分析所产生的要求。进行了用户调查,以确定这些工具在实践中是否可用且对试验人员有帮助。此外,我们还调查了测试阶段是否揭示了最初未考虑的招募工具的其他要求。

方法

我们对来自 3 家德国大学医院的 10 名参与者进行了半结构化访谈,这些参与者在目前正在招募的试验中至少使用了 1 个月的患者招募工具 KAS+或 recruIT。然后,使用 Meyring 方法对访谈进行转录和分析。将受访者的陈述分为 5 组要求,并按其频率进行排序。

结果

评估的 recruIT 和 KAS+工具分别满足了之前确定的 12 个要求中的 7 个和 11 个要求。接受采访的参与者提到需要不同的通知时间表、与他们的工作流程集成、不同的患者特征以及匿名筛选清单。这导致了对患者招募系统实施或增强的新要求列表。

结论

试验人员报告说,他们非常需要支持来确定合格的试验参与者。此外,患者招募中的工作流程因试验而异。为了使招募系统更适合不同类型试验的工作流程,我们建议为筛选清单实施可调整的通知时间表、详细的工作流程分析、广泛的患者筛选选项以及显示识别清单上人员所需的所有信息。尽管存在批评,但所有参与者都确认将再次使用患者招募系统。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0b6b/11470215/f94c449d408b/humanfactors_v11i1e56872_fig1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验