基于人工智能的临床决策支持系统的参与式设计所面临的挑战和促进方法:范围审查协议》。

IF 1.4 Q3 HEALTH CARE SCIENCES & SERVICES JMIR Research Protocols Pub Date : 2024-09-05 DOI:10.2196/58185
Tabea Rambach, Patricia Gleim, Sekina Mandelartz, Carolin Heizmann, Christophe Kunze, Philipp Kellmeyer
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引用次数: 0

摘要

背景:最近几年,人们对开发基于人工智能(AI)的临床决策支持系统(CDSS)越来越感兴趣。然而,要在实践中成功实施这些系统还存在一些障碍,包括这些系统缺乏认可度。参与式方法旨在让未来的用户参与设计临床决策支持系统(CDSS)等应用程序,使其更易于接受、更可行、从根本上更贴近实践。然而,基于人工智能的技术发展对用户参与过程和相关方法提出了挑战:本综述旨在总结和介绍临床医生参与研究和开发基于人工智能的决策支持系统的主要方式、方法、实践和具体挑战:本综述将采用乔安娜-布里格斯研究所(Joanna Briggs Institute)的综述方法。我们通过 PubMed、ACM Digital Library、Cumulative Index to Nursing and Allied Health 和 PsycInfo 等数据库对符合条件的研究进行了检索。每个数据库都使用了以下搜索过滤器:时间为 2012 年 1 月 1 日至 2023 年 10 月 31 日,仅限英语和德语研究,有摘要。范围界定审查将包括涉及基于人工智能的 CDSS(混合型和数据驱动型人工智能方法)的开发、试点、实施和评估的研究。临床工作人员必须以参与的方式参与其中。在进行数据检索的同时,还将进行人工灰色文献检索。然后将潜在的出版物导出到参考文献管理软件中,并删除重复的文献。之后,获得的论文集将转入系统综述管理工具。将对所有出版物进行筛选、提取和分析:标题和摘要筛选将由两名独立审稿人进行。如有异议,将由第三位审稿人参与解决。将使用为研究准备的数据提取工具提取数据:本范围综述协议于 2023 年 3 月 11 日在开放科学框架注册。当时全文筛选工作已经开始。在通过标题和摘要筛选出的 3118 项研究中,有 31 项被纳入全文筛选。数据收集和分析以及稿件准备工作计划于 2024 年第二和第三季度进行。稿件应于 2024 年底提交:本综述将描述基于人工智能的决策支持系统参与式开发的知识现状。目的是找出知识差距并提供研究动力。国际注册报告标识符(irrid):DERR1-10.2196/58185。
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Challenges and Facilitation Approaches for the Participatory Design of AI-Based Clinical Decision Support Systems: Protocol for a Scoping Review.

Background: In the last few years, there has been an increasing interest in the development of artificial intelligence (AI)-based clinical decision support systems (CDSS). However, there are barriers to the successful implementation of such systems in practice, including the lack of acceptance of these systems. Participatory approaches aim to involve future users in designing applications such as CDSS to be more acceptable, feasible, and fundamentally more relevant for practice. The development of technologies based on AI, however, challenges the process of user involvement and related methods.

Objective: The aim of this review is to summarize and present the main approaches, methods, practices, and specific challenges for participatory research and development of AI-based decision support systems involving clinicians.

Methods: This scoping review will follow the Joanna Briggs Institute approach to scoping reviews. The search for eligible studies was conducted in the databases MEDLINE via PubMed; ACM Digital Library; Cumulative Index to Nursing and Allied Health; and PsycInfo. The following search filters, adapted to each database, were used: Period January 01, 2012, to October 31, 2023, English and German studies only, abstract available. The scoping review will include studies that involve the development, piloting, implementation, and evaluation of AI-based CDSS (hybrid and data-driven AI approaches). Clinical staff must be involved in a participatory manner. Data retrieval will be accompanied by a manual gray literature search. Potential publications will then be exported into reference management software, and duplicates will be removed. Afterward, the obtained set of papers will be transferred into a systematic review management tool. All publications will be screened, extracted, and analyzed: title and abstract screening will be carried out by 2 independent reviewers. Disagreements will be resolved by involving a third reviewer. Data will be extracted using a data extraction tool prepared for the study.

Results: This scoping review protocol was registered on March 11, 2023, at the Open Science Framework. The full-text screening had already started at that time. Of the 3,118 studies screened by title and abstract, 31 were included in the full-text screening. Data collection and analysis as well as manuscript preparation are planned for the second and third quarter of 2024. The manuscript should be submitted towards the end of 2024.

Conclusions: This review will describe the current state of knowledge on participatory development of AI-based decision support systems. The aim is to identify knowledge gaps and provide research impetus. It also aims to provide relevant information for policy makers and practitioners.

International registered report identifier (irrid): DERR1-10.2196/58185.

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