Bharadwaj Prateek, Berger Michael, Blumer Steven L, Lobig Franziska
Bayer AG, Berlin, Germany.
Simon-Kucher & Partners Strategy & Marketing Consultants GmbH, Bonn, Germany.
J Imaging Inform Med. 2024 Dec;37(6):2740-2751. doi: 10.1007/s10278-024-01146-2. Epub 2024 Jun 14.
In the rapidly evolving digital radiology landscape, a surge in solutions has emerged including more than 500 artificial intelligence applications that have received 510 k clearance by the FDA. Moreover, there is an extensive number of non-regulated applications, specifically designed to enhance workflow efficiency within radiology departments. These efficiency applications offer tremendous opportunities to resolve operational pain points and improve efficiency for radiology practices worldwide. However, selecting the most effective workflow efficiency applications presents a major challenge due to the multitude of available solutions and unclear evaluation criteria. In this article, we share our perspective on how to structure the broad field of workflow efficiency applications and how to objectively assess individual solutions. Along the different stages of the radiology workflow, we highlight 31 key operational pain points that radiology practices face and match them with features of workflow efficiency apps aiming to address them. A framework to guide practices in assessing and curating workflow efficiency applications is introduced, addressing key dimensions, including a solution's pain point coverage, efficiency claim strength, evidence and credibility, ease of integration, and usability. We apply this framework in a large-scale analysis of workflow efficiency applications in the market, differentiating comprehensive workflow efficiency ecosystems seeking to address a multitude of pain points through a unified solution from workflow efficiency niche apps following a targeted approach to address individual pain points. Furthermore, we propose an approach to quantify the financial benefits generated by different types of applications that can be leveraged for return-on-investment calculations.
在快速发展的数字放射学领域，各种解决方案如潮水般涌现，其中包括500多种已获得美国食品药品监督管理局（FDA）510(k)许可的人工智能应用程序。此外，还有大量未经监管的应用程序，专门用于提高放射科的工作流程效率。这些提高效率的应用程序为解决全球放射科实践中的操作痛点和提高效率提供了巨大机遇。然而，由于可用解决方案众多且评估标准不明确，选择最有效的工作流程效率应用程序面临重大挑战。在本文中，我们分享了关于如何构建工作流程效率应用程序这一广泛领域以及如何客观评估单个解决方案的观点。在放射科工作流程的不同阶段，我们突出了放射科实践面临的31个关键操作痛点，并将它们与旨在解决这些痛点的工作流程效率应用程序的功能相匹配。本文介绍了一个指导评估和筛选工作流程效率应用程序的框架，涉及关键维度，包括解决方案的痛点覆盖范围、效率声明强度、证据和可信度、集成难易程度以及可用性。我们将这个框架应用于对市场上工作流程效率应用程序的大规模分析，区分了通过统一解决方案来解决众多痛点的综合工作流程效率生态系统和采用针对性方法解决单个痛点的工作流程效率细分应用程序。此外，我们提出了一种量化不同类型应用程序产生的财务效益的方法，可用于投资回报率计算。
