Awad Selvana, Loveday Thomas, Lau Richard, Baysari Melissa T
Biomedical Informatics and Digital Health, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Australia.
eHealth NSW, New South Wales, Australia.
Mayo Clin Proc Digit Health. 2024 Nov 27;3(1):100182. doi: 10.1016/j.mcpdig.2024.11.003. eCollection 2025 Mar.
To develop a vendor-agnostic, human factors (HF)-based guideline to guide the design, evaluation, and continuous improvement of clinical decision support (CDS).
The study used a 2-phased iterative approach between June 2022 and June 2024. Phase 1 involved a search for relevant industry standards and literature and consultation with multidisciplinary subject matter experts. Phase 2 involved a workshop with 30 health care and academic stakeholders to evaluate face validity and perceived usefulness of the initial section of the guideline. Participants were asked if the guideline met their expectations, to report on usefulness and ease of use and to suggest areas for improvement.
Phase 1 resulted in a compilation of accessible, best practice, and context-appropriate HF guidance for CDS design and optimization. The guideline supports users in determining whether use of CDS is appropriate, and if yes, CDS options and design guidance. During phase 2, the guideline addressed 15 of participants' 19 expectations for a CDS guideline. Participants said the guideline was helpful, comprehensive, easy to use, and provided step-by-step guidance, boundaries, and transparency around CDS decisions. Participants recommended strengthening guidance around the need to understand system capabilities and the technical burden or complexity of CDS, and further guidance on how to approach CDS optimization using the guideline.
The 2-phased iterative development and feedback process resulted in the development of an HF-informed guideline to provide consolidated, accessible, and current best practice guidance on the appropriateness of CDS and CDS options, as well as designing, evaluating, and continuously improving CDS. Future work will evaluate the impact and implementation of the guideline in real-world settings.
制定一项与供应商无关的、基于人为因素(HF)的指南,以指导临床决策支持(CDS)的设计、评估和持续改进。
该研究在2022年6月至2024年6月期间采用两阶段迭代方法。第一阶段包括搜索相关行业标准和文献,并与多学科主题专家进行磋商。第二阶段包括与30名医疗保健和学术利益相关者举办一次研讨会,以评估该指南初稿的表面效度和感知有用性。询问参与者该指南是否符合他们的期望,报告其有用性和易用性,并提出改进领域。
第一阶段形成了一份关于CDS设计和优化的可获取、最佳实践且符合实际情况的HF指南汇编。该指南帮助用户确定是否适合使用CDS,如果适合,还提供CDS选项和设计指南。在第二阶段,该指南满足了参与者对CDS指南的19项期望中的15项。参与者表示,该指南很有帮助、全面、易于使用,并提供了关于CDS决策的逐步指导、界限和透明度。参与者建议加强关于理解系统能力以及CDS的技术负担或复杂性的必要性的指导,以及关于如何使用该指南进行CDS优化的进一步指导。
两阶段迭代开发和反馈过程产生了一项基于HF的指南,以提供关于CDS及其选项的适用性、以及设计、评估和持续改进CDS的综合、可获取且最新的最佳实践指导。未来的工作将评估该指南在实际环境中的影响和实施情况。